Chinese leaker &quot;Golden Pig Upgrades&quot; leaks information on the upcoming product spread of AMD&#x27;s Zen 5 architecture, including specifics on highly anticipated Strix Point and Strix Halo.

Golden Pig squeals on AMD's Zen 5 lineup, reveals ten-core Strix Point chips | Tom's Hardware

A new patch series sent out today by AMD Linux engineers confirm that PCIe TPH will be supported with &#x27;upcoming AMD hardware&#x27; as a nice performance optimization feature for PCI Express.

AMD Preparing PCIe TPH Support For Upcoming CPUs - Phoronix

The investing community is buzzing with AMD stock.

Why Is Everyone Talking About AMD Stock?

As expected, AMD today published the Micro Engine Scheduler &#x27;MES&#x27; firmware documentation for RDNA3 graphics processors as part of better engaging with the open-source community and aiming to address some gaps in their open-source GPU compute stack.

AMD Publishes Micro Engine Scheduler "MES" Firmware Documentation - Phoronix

An Asus laptop listing has revealed the new branding for a new line of AMD processors, with the company going big on AI capabilities.

AMD’s new Ryzen CPU name just leaked, and it’s not what you expect

AMD&#x27;s powerful 3D V-Cache tech is now more affordable than ever.

AMD Ryzen 7 5700X3D Review: A Value Gaming Masterpiece | Tom's Hardware

Barron&#x27;s is a leading source of financial news, providing in-depth analysis and commentary on stocks, investments and how markets are moving across the world.

Nvidia Stock Rises. Why AMD Might Be a More Tempting AI Chip Bet.

AMD might have let a cancelled engineering experiment slip into the wild.

AMD Ryzen 9 7950X3D Spotted With Double the V-Cache at 192MB | Extremetech

AMD has fallen by nearly one-third over the last two months.

Is AMD Stock a Buy Now? @themotleyfool #stocks $AMD

AMD is strategically preparing to meet the demand for AI computing by leveraging its product range and open software capabilities. Their investment in R&amp;D has quadrupled and they have increased their market presence, especially after acquiring Xilinx. Despite concerns about gaming segment weakness, ...

AMD Positions for AI Leadership with Expanded Portfolio, Quadrupled R&D Investment Ahead of Major Computex Announcements - Advanced Micro Devices (NASDAQ:AMD) - Benzinga

Advanced Micro Devices Inc. (AMD)

Advanced Micro Devices Inc.’s ( ) latest earnings report highlights the company’s impressive performance despite several challenges being faced by the PC industry AMD For the entire year, AMD’s yearly revenue increased by 44% to $23.6 billion. In terms of the company’s financial performance for the fourth quarter, revenue climbed 16% over the prior year to $5.6 billion. The company's sales of embedded products also grew from $3.9 billion in 2021 to $10.6 billion in 2022. Although the fourth quarter saw a 51% decline in revenue for AMD's client segment, which includes PCs, this decline was offset by a 42% increase in revenue for AMD's data center segment. After seeing a rapid expansion during the pandemic, the PC industry as a whole is currently experiencing a significant downturn in sales. CEO Lisa Su attributed these results to significant growth in AMD’s embedded and data center segments, which she said accounted for more than 50% of overall revenue in the quarter. The debut of a new CPU by AMD and its pandemic-era acquisition of specialty chip manufacturer Xilinx is also helping AMD's data center business thrive. “We set annual records for revenue, gross margin and profitability driven largely by a 64% increase in our data centre segment revenue and the strong performance of our embedded segment following our Xilinx acquisition.” Furthermore, AMD's strategic partnerships with major cloud providers have further solidified its position in the data center market, Lisa Su said on the call. “Turning to the fourth quarter business results, starting with our data center segment. Revenue increased 42% year over year to $1.7 billion, led by increased adoption of our EPYC processors by cloud providers. In cloud, sales to North American hyperscalers more than doubled year over year as hyperscale customers continued moving more of their internal workloads and external instances to EPYC processors.” Since being bought a year ago, Xilinx has contributed to AMD’s record sales, driven mostly by high demand for Alveo X3 series boards from the financial services industry. Future Outlook Bloomberg Intelligence analyst estimates that earnings for chip companies, more generally, will decrease 21% this year, but despite this widespread concern about a downturn in the chip industry, AMD is expected to be among the few semiconductor companies to experience growth in 2023. Although AMD on Tuesday forecasted $5.3 billion in sales for the quarter ending in March, a 10% decline in revenue over the prior year, the firm anticipates a stronger second half of this year. Su expects the first quarter to be the bottom for the company in PCs and affirmed that they would grow from there into the second quarter and then into the second half While referring to the company’s earnings for 2022, Su said: "2022 was a strong year for AMD as we delivered best-in-class growth and record revenue despite the weak PC environment in the second half of the year.” Despite not providing full-year forecasts for the company due to economic uncertainty, Su remained certain about the company’s performance outlook. "Although the demand environment is mixed, we are confident in our ability to gain market share in 2023, and deliver long-term growth based on our differentiated product portfolio.” AMD is also participating in the competition for AI. With a couple of new product releases specifically focused on powering AI engines, including the Ryzen 7000 series and MI300 accelerator, Su acknowledged, on the call, the company's focus on AI over the next years, “Over the next several years, one of our largest growth opportunities is in AI, which is in the early stages of transforming virtually every industry service and product.” She further expressed assurance that AMD has a robust product lineup to compete in the AI business. “We expect AI adoption will accelerate significantly over the coming years and are incredibly excited about leveraging our broad portfolio of CPUs, GPUs and adaptive accelerators in combination with our software expertise to deliver differentiated solutions that can address the full spectrum of AI needs in training and inference across cloud, edge and client.” Already, more than 100 of the fastest supercomputers in the world are powered by AMD. The fourth-generation EPYC uses up to 80% less energy. AMD also has 140 fourth-generation EPYC platforms for businesses that are being built by HPE, Dell, Lenovo, Super Micro, and other companies. Overall, Su maintains the stance that the company has an adaptive product portfolio that will enable them to grow revenue and gain market share in 2023.

AMD Posts Q4 Earnings Growth, Despite Industry Challenges; Expects to Capitalize on AI Demand

AMD পোস্টের Q4 আয়ের বৃদ্ধি, শিল্পের চ্যালেঞ্জ সত্ত্বেও; AI চাহিদাকে পুঁজি করার প্রত্যাশা করে

AMD verzeichnet trotz Branchenherausforderungen Gewinnwachstum im vierten Quartal; Erwartet, von der KI-Nachfrage zu profitieren

AMD는 업계의 어려움에도 불구하고 4분기 실적 성장을 기록했습니다. AI 수요 활용 기대

AMD, Sektördeki Zorluklara Rağmen 4. Çeyrekte Kar Artışını Açıkladı; Yapay Zeka Talebinden Yararlanmayı Bekliyor

has shot itself in the foot. After spending years exclusively dominating the graphic card market, the Santa Clara, Calif.-based chipmaker has fallen prey to predatory pricing, stagnation, and, quite frankly, the same shenanigans that 's dominance in the CPU market. Nvidia hurt Intel There's a new competitor in town, and while it has yet to completely usurp Nvidia's lead in the GPU market, it has already started chipping away at its market share. Yes, we're talking about (who else!?), which is likely going to be the next GPU of choice for nearly a quarter of HackerNoon readers that participated in a poll asking which graphic card they were going to buy in the future. AMD To be sure, Nvidia still commands respect, with nearly a third of users choosing to go team green when they buy their next GPU. However, opting for team red was virtually unheard of half a decade ago, even when AMD the long-awaited RX Vega GPUs to compete with Nvidia's wildly popular 10-series cards. announced For the most part, and had dominated the and markets from the early 2000s up until the late 2010s, relegating AMD to a niche given how poorly its chips performed. It didn't really help that AMD was slow to launch new products, and when it did, they would suffer from and , leaving it curiously absent from both the CPU and GPU markets for years on end. Intel Nvidia CPU GPU high power consumption bad thermals Perhaps the only thing that kept AMD afloat during those years were the custom-made chipsets it manufactured for consoles, in particular the Playstation and Xbox, which ultimately ended up becoming a for the Nasdaq-listed firm. But other than that, AMD really hadn't launched any products that had consumers' heads turning. significant source of revenue All of this changed with the 2017 launch of the Ryzen series processors that were based on a new architecture called Zen. Though as its Intel counterpart in gaming, the flagship product from that launch — the Ryzen 7 1800X — came at a spitting distance of Intel's then leader, the i7-8700k, in productivity tasks at a much, much lower price. not necessarily as powerful Meanwhile, less powerful variants of the Ryzen 7 had a similar story to tell: they were cheaper, performed nearly as well as their Intel counterparts in productivity, and notably, came with so budget-conscious consumers didn't have to spend extra. Consumers entering the Ryzen ecosystem also knew that every subsequent processor launch would continue to be based on the , meaning they could simply swap out their older processors for newer ones without . good heatsinks AM4 chipset necessarily changing motherboards Tech enthusiasts hailed the launch of the Ryzen 7 processors due to their potential for in the CPU market, which they did. Every subsequent processor launched since the original Ryzen 7 series has shown , while Intel's processors continue to provide . In fact, a HackerNoon editor was so impressed with the latest AMD processor launch (The Ryzen 9 7900X) that they decided to do the unthinkable: migrate to team red, citing not only the better performance, but, the better economics of being a part of the AMD ecosystem. breaking Intel's monopoly improvements in leaps and bounds incremental benefits over their predecessors AMD has also made gains in the GPU market, starting from the launch of Vega 64, which, while against Nvidia's 10 series products, couldn't necessarily compete with it because of how great the 10 series cards were in terms of both price performance. Vega 64 was also plagued by at launch, though AMD was quick to address those issues as they came along. reviewing modestly and driver issues But Nvidia got smug with the launch of the successor to the 10 series, perhaps hoping that consumers and tech enthusiasts would fall for its marketing and a promise for better-looking games with the ray-tracing feature. Sadly for Nvidia, , noting at the time that the 20 series cards were only incrementally better than their predecessors and the ray-tracing feature that Nvidia was charging a premium for was yet to be available across major video game titles. We personally termed the release as a 'meme launch,' signifying that anyone with a bit of time and research could figure out that the card was simply meant to make money and didn't really offer anything beyond a promise of a technology that had yet to reach fruition. Not to mention, it wasn't even at said technology (ray tracing). reviewers panned the release that great Meanwhile, AMD still hadn't hit the mark, with the RX 5000 series cards that were supposed to compete with Nvidia's 20-series products in power consumption, thermals, and drivers. As the 30-series launch came along and Nvidia seemed to have course corrected, AMD too did not disappoint, with the RX 6000 series from reviewers for its performance, price, and . facing issues getting praise availability Which brings us to the present: the Nvidia 4-series. And oh my god, it looks like history is repeating itself. This is yet another 'meme release' panned by reviewers for price, performance, and (not to mention ). Meanwhile, AMD's upcoming RX 7000 series cards are already touted to be than what Nvidia has put out. power consumption the issue that shall not be named cheaper and better https://www.youtube.com/watch?v=i2_xTUshy94?embedable=true All of this to say: we get it. Nvidia, just like Intel, has become a fat, lazy dragon content with its dominance from the glory days. In contrast, AMD has consistently targeted consumers that were long forgotten by the titans: the budget gamers, who, by all accounts, make up an . By positioning itself as the to market behemoths, AMD has been able to claw away at its competition, all the while investing money in R&D so it too could produce products that could compete with some of the best tech out there for cheaper. overwhelming majority of PC users less expensive alternative Surprisingly, a very small portion of HackerNoon readers might also opt for an Intel graphic card. While not exactly known for their graphical prowess, Intel's integrated GPUs have long been a mainstay of laptops, particularly at the lower price point. Reviewers have hailed the launch of Intel's Arc series desktop GPUs for the same reasons as they did for AMD's processors: , even if team blue's new product is . With Intel's entry into the desktop GPU market, we can now only hope for a gold trifecta in which no single company calls the shots, and all three competitors keep their peers in check with performance and pricing. Only time will tell. competition subpar at best To wrap things up, while we know what team HackerNoon readers are leaning toward if they want to purchase a graphic card, we also know (based on the poll results) that a good chunk of readers are unlikely to buy a new graphic card anytime soon. It could be the price, the availability, or the fact that they probably don't care about a graphic card and are likely not consumers who have a need for a dedicated GPU. , they follow a simple principle: they don't upgrade unless what they currently have is no longer giving them the performance they seek. Or To participate in Hackernoon’s current poll, head down to the following . link

Nearly 25% of HackerNoon Readers to Opt for AMD GPUs Next

Casi el 25 % de los lectores de HackerNoon optarán por las GPU de AMD

Près de 25% des lecteurs de HackerNoon opteront pour les GPU AMD Suivant

हैकरनून के लगभग 25% पाठक अगले एएमडी जीपीयू को चुनेंगे

HackerNoon の読者の 25% 近くが AMD GPU を選ぶ

Quase 25% dos leitores do HackerNoon optarão por GPUs AMD em seguida

Gần 25% người đọc HackerNoon chọn GPU AMD

接下来将近 25% 的 HackerNoon 读者选择 AMD GPU

The latest-gen gaming hardware promised to make a real difference in the gaming and technological world. This new generation of gaming included the latest PlayStation 5, Xbox Series X/S, the Nvidia RTX 30-series lineup, and the AMD RDNA2 series of GPUs. Even though it has been around six months since their release, most consumers still haven't gotten a chance to upgrade their systems/setups for many reasons, including limited stock availability. Especially in the PC GPU market, prices of GPUs skyrocketed just after their releases, and currently, you can hardly get your hands on one of these latest-gen GPUs. Finding one latest-gen GPU for their launch prices or at least for reasonable prices is impossible, and finding stock of them is  similarly hard. So what might be really causing this massive worldwide GPU shortage? Is Cryptocurrency Mining the Reason for the GPU Shortage? As GPUs have gone scarce as hens' teeth, people have already started pointing fingers at who they think might be responsible for this historical shortage. Consumers, especially gamers, have started blaming crypto miners for the worldwide GPU shortage, but without proper evidence, the question of who caused the real shortage still stands. It is not a secret that cryptocurrency mining has become a flourishing market, and the number of investors for it grows by the day. Especially the COVID-19 global pandemic encouraged more miners to join the market. This ultimately caused a great demand for GPUs in 2020. It is also a known fact that crypto mining usually relies on the raw processing power of GPUs, and has increased the demand for GPUs. Fortunately for the miners, the leading manufacturers in the GPU market, Nvidia and AMD, decided to release their next-gen GPU lineup around the same time. Both the latest GPU lineups from AMD and Nvidia were ideal for crypto miners as this new-gen was a massive leap from the previous gen, performance, and power consumption wise. the increasing crypto miner growth As a result, miners now empty the GPU stocks as soon as they restock or a new GPU gets released. They often buy not one but multiple GPUs for their mining farms (rigs), leaving the rest of the consumers angry and empty-handed. Worst of all mining usually runs these GPUs at the full potential 24/7, and this causes their silicon to degrade faster and die quickly. But the question still stands, are miners the real reason for the shortage? The answer to that question is as complicated as the crypto-mining  itself. Even though crypto miners contribute to a huge share of the current GPU demand, they aren't exactly the only reason or even the main reason for the shortage. Just like in all other markets, the global pandemic has also affected the GPU market by creating a higher demand and restricting the supply. The pandemic has forced people to stay at home and work from home, and most people have made this an opportunity to either upgrade their PC setups or build new ones altogether. This has greatly increased the demand for GPUs in the mid to late 2020. The issue still exists after more than a year since the pandemic affected our day-to-day lives. Not only the pandemic but also other industries that use silicon chips have contributed to this great GPU shortage. Even though experts show that miners aren't exactly the real reason behind the shortage, they could become a real threat to the future of the tech world as crypto mining consumes GPUs faster. The Increased Demand Silicon is a main component/resource in the manufacturing process of all sorts of processors and other computer chips. With the introduction of many smart gadgets and electric vehicles in the past few years, the demand for silicon chips has increased considerably. The problem is that even though the demand for silicon chips increases by the day, the manufacturing of silicon chips or semiconductors has deteriorated with the complications that came with the COVID-19 pandemic. Taiwan Semiconductor Manufacturing Company Limited and Intel Corporation are two of the main semiconductor producing companies in the world. They provide the majority of worldwide silicon needs. Unfortunately, the pandemic added restrictions to the production of silicon indirectly. This has caused the manufacturers to limit the supply of chips fulfilling only a fraction of the current demand. Fortunately, most of these issues are being overcome by the day. The manufacturing processes should all return to normal in a matter of time. But will it still be enough to overcome the shortage? Rich Miners The limited supply and the increased demand have really made the new GPU prices skyrocket soon after their releases. This has also made a marketplace for scalpers who sell these GPUs at great prices. Most gamers/consumers are also restricted by these price increases. This is not really a problem for miners as they are ready to spend thousands of dollars on GPUs. They usually earn more from them. This leaves average consumers no real choice but to cave in. They must buy an extremely overpriced GPU or settle for a used or an older GPU. So as long as miners are actively hunting for GPUs in the current market, gamers really have no choice but to wait. But what if alternatives for miners are introduced, or the performance of the latest-gen GPUs is cut off in mining processes? Nvidia Limits Crypto Mining on their Latest RTX 3060 RTX 3060 was Nvidia's fifth card in the 30-series lineup, and Nvidia tried to tackle crypto miners with its release. They stated that the software for its upcoming GeForce RTX 3060 would be . This made the card less appealing for Ethereum miners Ethereum is one of the biggest GPU-mineable cryptos in the current world. Instead, Nvidia plans on introducing a range of crypto-currency mining processors (CMPs) to counter the GPU demand by miners. However, it again raises a question if Nividia is greedy by utilizing the most important finite resource in the market, silicon, on CMPs when they could be using them on increasing the production of consumer-grade GPUs in the first place. cutting off Ethereum mining efficiency by 50% When Will Things Get Back to Normal? Still, the demand for GPUs easily exceeds the supply. This is predicted to last for at least another year. The semiconductor manufacturers are slowly recovering, and things are getting back to normal but really slowly. The semiconductor demand comes from not only the GPU manufacturers but all sorts of tech industries such as mobile phones, CPUs, and even automobiles. Experts and Nvidia themselves predict that shortages likely to continue at least until Q4 2021, if not into 2022, and AMD fans can expect the same. Read more about Nvidia's statement about how "crypto mining didn't really affect" their RTX 30-series launch from here. Solutions in the Meantime If you are looking to upgrade your PC or building a brand new one but reluctant to spend ridiculous amounts on GPUs, there is still hope for you in the current market. The best thing to do right now is to settle for an older-gen GPU or a used one until the new-gen GPUs come back in stock and their prices decrease. A last-gen GPU, an RTX 20-series card, or an RX 5000 series card is just fine for today's gaming and professional needs. However, you might still find it a little hard to find a brand new previous gen card, so the ideal solution is to go with a used card. If you search hard enough, you might be able to find a decent mid/high-end GPU for a reasonable price amongst scalper prices. The latest-gen Nvidia RTX 30-series cards and AMD Radeon RX 6000-series cards on their own are pretty hard to find. But there are many sites and manufacturers that offer pre-built systems with these new graphics cards. The best part about them is that compared to spending thousands on the GPU alone, these pre-built PCs are reasonably priced too. If you don't mind compromising some performance for portability and convenience, gaming laptops are also a great way to get your hands on the latest-gen GPUs. And if you really want to get your hands on a juicy RTX 3080 or an RTX 3070, or an RX 6700XT, you can wait out the shortage until prices start to settle down and stocks start to pile up. The COVID-19 restrictions will eventually lift, and things will come back to normal eventually. Unfortunately, this won't be happening anytime soon. If you are still determined to wait it out, you might even be able to save an extra few bucks to go for an upgraded version when things come back to normal. If you haven't got the patience, you can check out the current and find a good deal. RTX 3070 pricing

The Shortage of AMD and Nvidia GPUs Causes Gamers Grief

Want to train machine learning models on your Mac’s integrated AMD GPU or an external graphics card? Look no further than PlaidML. Anyone who has tried to train a neural network with TensorFlow on macOS knows that the process kind of sucks. TensorFlow can only leverage the CPU on Macs, as GPU-accelerated training requires an Nvidia chipset. Most large models take orders of magnitude more time to train on a CPU than on even a simple GPU. To make matters worse, many Macs have powerful discreet AMD GPUs that are forced to sit idle while training. TensorFlow only supports Nvidia devices, which are incompatible with macOS. This is where plaidML comes in. Rather than pay for time on a cloud-based system or purchase a new machine, you can install PlaidML and use it to train Keras models right on your Mac’s graphics processor. TensorFlow serves as a backend for Keras, interpreting Keras’ high-level Python syntax and converting it to instructions that can be executed in parallel on specialized hardware like a GPU. PlaidML is an alternative backend for Keras that supports parallelization frameworks other than Nvidia’s CUDA. On a Mac, you can use PlaidML to train Keras models on your CPU, your CPU’s integrated graphics, a discreet AMD graphics processor, or even an external AMD GPU connected via Thunderbolt 3. I first started poking around with PlaidML because I was looking for a way to train a deep convolutional neural network on a very large image dataset. I attempted to do this in Google’s Colab, but the online tool proved to be very frustrating for long-running jobs. I had a gathering dust, so I wanted a way to use it to train models locally with my MacBook. Radeon RX580 eGPU After a few quick steps, I was up and running with PlaidML. Here’s how you can use it on your system. First, . I highly recommend using virtual environments here to isolate your PlaidML installation from the rest of your system. install PlaidML via pip PlaidML’s power comes with its simplicity. After installation, activating your GPU is as simple as running plaidml-setup After selecting whether or not you want to enable experimental features, this tool will ask you which computational device you’d like to use. You should see a list like the following: 1 : llvm_cpu.0
2 : metal_intel(r)_hd_graphics_530.0
3 : metal_amd_radeon_pro_450.0
4 : metal_amd_radeon_rx_580.0 The first option is my CPU, the second is the Intel integrated graphics inside my CPU, the third option is the discreet AMD GPU in my 15" MacBook Pro, and the fourth option is my RX 580 eGPU. I absolutely love how easy it is to switch processors; this allows me to train simple models on-the-go with my laptop’s discreet GPU and use my eGPU for heavier tasks. The only caveat here is you no longer have access to TensorFlow features such as TensorFlow Datasets. All code you write needs to be in pure Keras. I haven’t found this to be much of a limitation, and it leads to more portable software anyway. PlaidML also works with Nvidia GPUs, so if you work on a team that uses different GPU architectures PlaidML makes things very easy. Using PlaidML as Keras’ backend is as simple as the following: os environ
environ[ ] = keras from import "KERAS_BACKEND" "plaidml.keras.backend" import And that’s literally it. Below is a complete example that you can try on your own system after installing PlaidML. It trains a very simple neural network with one hidden layer that sums input vectors. numpy np os environ
environ[ ] = keras keras.layers Dense matplotlib pyplot plt num_samples = ; vect_len = ; max_int = ; min_int = ; X = np.random.randint(min_int, max_int, (num_samples, vect_len))
Y = np.sum(X, axis= ) split_idx = int( * len(Y))
train_X = X[:split_idx, :]; test_X = X[split_idx:, :]
train_Y = Y[:split_idx]; test_Y = Y[split_idx:] model = keras.models.Sequential()
model.add(keras.layers.Dense( , activation= , input_shape=(vect_len,)))
model.add(keras.layers.Dense( ))
model.compile( , )

history = model.fit(train_X, train_Y, validation_data=(test_X, test_Y), \
                    epochs= , batch_size= ) plt.plot(history.history[ ])
plt.plot(history.history[ ])
plt.title( )
plt.ylabel( )
plt.xlabel( )
plt.legend([ , ], loc= )
plt.show() import as from import "KERAS_BACKEND" "plaidml.keras.backend" import from import from import as # Params 100000 20 10 1 # Generate dataset 1 # Get 80% of data for training 0.8 # Make model 32 'relu' 1 'adam' 'mse' 10 100 # summarize history 'loss' 'val_loss' 'model loss' 'loss' 'epoch' 'train' 'test' 'upper left' You can try this on different computing devices. You will likely find that training this model on your CPU is faster, as the dataset is very small and the model very simple. You will notice a significant speedup for more complex models, however. You can find some heavier tests on the . PlaidML GitHub page So far, PlaidML has been awesome. Intel now in some way with PlaidML’s development, so hopefully this will ensure that PlaidML is around for a long time. appears to be involved I hope you found this article useful. Check out . more of my writing here Also published at https://alexwulff.medium.com/machine-learning-on-macos-with-an-amd-gpu-and-plaidml-55a46fe94bc0

Use plaidML to do Machine Learning on macOS with an AMD GPU

Benchmarking Apache Kafka: Performance-per-price

বেঞ্চমার্কিং অ্যাপাচি কাফকা: পারফরম্যান্স-প্রতি-মূল্য

Benchmarking von Apache Kafka: Leistung pro Preis

Evaluación comparativa de Apache Kafka: rendimiento por precio

Analyse comparative d'Apache Kafka : performances par prix

अपाचे काफ्का की बेंचमार्किंग: प्रदर्शन-प्रति-मूल्य

Apache Kafka のベンチマーク: 価格に対するパフォーマンス

Apache Kafka 벤치마킹: 가격 대비 성능

Comparativo de mercado do Apache Kafka: desempenho por preço

Сравнительный анализ Apache Kafka: соотношение производительности и цены

Apache Kafka'nın Karşılaştırması: Fiyat başına performans

Đo điểm chuẩn Apache Kafka: Hiệu suất trên mỗi giá

Apache Kafka 基准测试：性价比

With the development of generative artificial intelligence and its practical applications, creating servers for artificial intelligence has become critical for various industries - from automotive manufacturing to medicine, as well as for educational and governmental institutions. Let's consider the most important components that affect the selection of a server for artificial intelligence: the central processing unit (CPU) and graphics processing unit (GPU). Selecting appropriate processors and graphics cards will enable you to set up a high-performance platform and significantly accelerate calculations related to artificial intelligence on a dedicated or virtual (VPS) server. Rent GPU servers with instant deployment or a server with a custom configuration with professional-grade NVIDIA Tesla H100 / H100 80Gb or RTX A5000 / A4000 cards. GPU servers with game RTX4090 cards are also available. How do you choose the right processor for your AI server? The processor is the main "calculator" that receives commands from users and performs "command cycles," which will yield the desired results. Therefore, a big part of what makes an AI server so powerful is its CPU. You might expect a comparison between AMD and Intel processors. Yes, these two industry leaders are at the forefront of processor manufacturing, with the lineup of Intel 5th generation Intel® Xeon® ( and already announced 6th generation) and AMD EPYC™ 8004/9004 representing the pinnacle of x86-based CISC processors. If you are looking for excellent performance combined with a mature and proven ecosystem, selecting top-of-the-line products from these chip manufacturers would be the right choice. If budget is a concern, consider older versions of Intel® Xeon® and AMD EPYC™ processors. Even desktop CPUs from AMD or Nvidia's higher-end models would be a good starting point for working with AI if your workload does not require a large number of cores and multithreading capabilities. In practice, when it comes to language models, the choice of graphics accelerator or the amount of RAM installed in the server will have a greater impact than the choice between CPU types. While some models, such as the 8x7B from Mixtral, can produce results comparable to the computational power of tensor cores found in video cards when run on a CPU, they also require 2-3 times more RAM than a CPU + GPU bundle. For instance, a model that runs on 16 GB of RAM and 24 GB of GPU video memory may require up to 64 GB of RAM when running solely on the CPU. In addition to AMD and Intel, there are other options available. These can be solutions based on ARM architecture, such as NVIDIA Grace™, which combines ARM cores with patented NVIDIA features, or Ampere Altra™. How do you choose the right graphics processing unit (GPU) for your AI server? The GPU plays an increasingly important role in AI server operations today. It serves as an accelerator that helps the CPU process requests to neural networks much faster and more efficiently. The GPU can break down tasks into smaller segments and perform them simultaneously using parallel computing or specialized cores. For example, NVIDIA's tensor cores provide orders of magnitude higher performance in 8-bit floating-point (FP8) calculations with Transformer Engine, Tensor Float 32 (TF32), and FP16, showing excellent results in high-performance computing (HPC). This is particularly noticeable not during inference (the operation of the neural network) but during training, as for example, for models with FP32, this process can take several weeks or even months. To narrow down your search criteria, consider the following questions: Will the nature of your AI server's workload change over time? Most modern GPUs are designed for very specific tasks. The architecture of their chips may be suitable for certain areas of AI development or application, and new hardware and software solutions can render previous generations of GPU obsolete in just a few years (1-2-3).
Will you mainly focus on training AI or inference (usage)? These two processes are the foundation of all modern AI iterations with limited memory budgets. During training, the AI model processes a large amount of data with billions or even trillions of parameters. It adjusts the "weights" of its algorithms until it can consistently generate correct results. In inference mode, the AI relies on the "memory" of its training to respond to new input data in the real world. Both processes require significant computational resources, so GPUs and expansion modules are installed for acceleration. Graphic processing units (GPUs) are designed specifically for training deep learning models with specialized cores and mechanisms that can optimize this process. For example, NVIDIA's H100 with 8 GPU cores provides more than 32 petaflops of performance in FP8 deep learning. Each H100 contains fourth-generation tensor cores using a new type of data called FP8 and a "Transformer Engine" for optimization. Recently, NVIDIA introduced the next generation of their GPUs, the B200, which will be even more powerful. A strong alternative to AMD solutions is the AMD Instinct™ MI300X. Its feature is a large memory capacity and high data bandwidth, which is important for inferencing-based generative AI applications, such as large language models (LLM). AMD claims that their GPUs are 30% more efficient than NVIDIA's solutions but have less mature software. If you need to sacrifice a bit of performance to fit within budget constraints or if your dataset for training the AI is not too large, you can consider other options from AMD and NVIDIA. For inferencing tasks or when continuous operation in 24/7 mode for training is not required, "consumer" solutions based on Nvidia RTX 4090 or RTX 3090 may be suitable. If you are looking for stability in long-term calculations for model training, you can consider NVIDIA's RTX A4000 or A5000 cards. Although the H100 with PCIe bus may offer a more powerful solution with 60-80% performance depending on the tasks, the RTX A5000 is a more accessible option and could be an optimal choice for certain tasks (such as working with models like 8x7B). For more exotic inference solutions, you can consider cards like AMD Alveo™ V70, NVIDIA A2/L4 Tensor Core, and Qualcomm® Cloud AI 100. In the near future, AMD and NVIDIA plan to outperform Intel's GPU Gaudi 3 in the AI training market. Considering all these factors and taking into account software optimization for HPC and AI, we recommend servers with Intel Xeon or AMD Epyc processors and GPUs from NVIDIA. For AI inferencing tasks, you can use GPUs from RTX A4000/A5000 to RTX 3090, while for training and working on multi-modal neural networks, it's advisable to allocate budgets for solutions from RTX 4090 to A100/H100. Rent GPU servers with instant deployment or a server with a custom configuration with professional-grade NVIDIA Tesla H100 / H100 80Gb or RTX A5000 / A4000 cards. GPU servers with game RTX4090 cards are also available. This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here. With the development of generative artificial intelligence and its practical applications, creating servers for artificial intelligence has become critical for various industries - from automotive manufacturing to medicine, as well as for educational and governmental institutions. Let's consider the most important components that affect the selection of a server for artificial intelligence: the central processing unit (CPU) and graphics processing unit (GPU). Selecting appropriate processors and graphics cards will enable you to set up a high-performance platform and significantly accelerate calculations related to artificial intelligence on a dedicated or virtual (VPS) server. Rent GPU servers with instant deployment or a server with a custom configuration with professional-grade NVIDIA Tesla H100 / H100 80Gb or RTX A5000 / A4000 cards. GPU servers with game RTX4090 cards are also available. Rent GPU servers with instant deployment or a server with a custom configuration with professional-grade NVIDIA Tesla H100 / H100 80Gb or RTX A5000 / A4000 cards. GPU servers with game RTX4090 cards are also available. Rent GPU servers with instant deployment Rent GPU servers with instant deployment custom configuration custom configuration GPU servers with game RTX4090 cards GPU servers with game RTX4090 cards How do you choose the right processor for your AI server? The processor is the main "calculator" that receives commands from users and performs "command cycles," which will yield the desired results. Therefore, a big part of what makes an AI server so powerful is its CPU. You might expect a comparison between AMD and Intel processors. Yes, these two industry leaders are at the forefront of processor manufacturing, with the lineup of Intel 5th generation Intel® Xeon® ( and already announced 6th generation) and AMD EPYC™ 8004/9004 representing the pinnacle of x86-based CISC processors. If you are looking for excellent performance combined with a mature and proven ecosystem, selecting top-of-the-line products from these chip manufacturers would be the right choice. If budget is a concern, consider older versions of Intel® Xeon® and AMD EPYC™ processors. Even desktop CPUs from AMD or Nvidia's higher-end models would be a good starting point for working with AI if your workload does not require a large number of cores and multithreading capabilities. In practice, when it comes to language models, the choice of graphics accelerator or the amount of RAM installed in the server will have a greater impact than the choice between CPU types. While some models, such as the 8x7B from Mixtral, can produce results comparable to the computational power of tensor cores found in video cards when run on a CPU, they also require 2-3 times more RAM than a CPU + GPU bundle. For instance, a model that runs on 16 GB of RAM and 24 GB of GPU video memory may require up to 64 GB of RAM when running solely on the CPU. In addition to AMD and Intel, there are other options available. These can be solutions based on ARM architecture, such as NVIDIA Grace™, which combines ARM cores with patented NVIDIA features, or Ampere Altra™. How do you choose the right graphics processing unit (GPU) for your AI server? The GPU plays an increasingly important role in AI server operations today. It serves as an accelerator that helps the CPU process requests to neural networks much faster and more efficiently. The GPU can break down tasks into smaller segments and perform them simultaneously using parallel computing or specialized cores. For example, NVIDIA's tensor cores provide orders of magnitude higher performance in 8-bit floating-point (FP8) calculations with Transformer Engine, Tensor Float 32 (TF32), and FP16, showing excellent results in high-performance computing (HPC). This is particularly noticeable not during inference (the operation of the neural network) but during training, as for example, for models with FP32, this process can take several weeks or even months. To narrow down your search criteria, consider the following questions: Will the nature of your AI server's workload change over time? Most modern GPUs are designed for very specific tasks. The architecture of their chips may be suitable for certain areas of AI development or application, and new hardware and software solutions can render previous generations of GPU obsolete in just a few years (1-2-3). Will you mainly focus on training AI or inference (usage)? These two processes are the foundation of all modern AI iterations with limited memory budgets. Will the nature of your AI server's workload change over time? Most modern GPUs are designed for very specific tasks. The architecture of their chips may be suitable for certain areas of AI development or application, and new hardware and software solutions can render previous generations of GPU obsolete in just a few years (1-2-3). Will you mainly focus on training AI or inference (usage)? These two processes are the foundation of all modern AI iterations with limited memory budgets. During training, the AI model processes a large amount of data with billions or even trillions of parameters. It adjusts the "weights" of its algorithms until it can consistently generate correct results. In inference mode, the AI relies on the "memory" of its training to respond to new input data in the real world. Both processes require significant computational resources, so GPUs and expansion modules are installed for acceleration. Graphic processing units (GPUs) are designed specifically for training deep learning models with specialized cores and mechanisms that can optimize this process. For example, NVIDIA's H100 with 8 GPU cores provides more than 32 petaflops of performance in FP8 deep learning. Each H100 contains fourth-generation tensor cores using a new type of data called FP8 and a "Transformer Engine" for optimization. Recently, NVIDIA introduced the next generation of their GPUs, the B200, which will be even more powerful. A strong alternative to AMD solutions is the AMD Instinct™ MI300X. Its feature is a large memory capacity and high data bandwidth, which is important for inferencing-based generative AI applications, such as large language models (LLM). AMD claims that their GPUs are 30% more efficient than NVIDIA's solutions but have less mature software. If you need to sacrifice a bit of performance to fit within budget constraints or if your dataset for training the AI is not too large, you can consider other options from AMD and NVIDIA. For inferencing tasks or when continuous operation in 24/7 mode for training is not required, "consumer" solutions based on Nvidia RTX 4090 or RTX 3090 may be suitable. If you are looking for stability in long-term calculations for model training, you can consider NVIDIA's RTX A4000 or A5000 cards. Although the H100 with PCIe bus may offer a more powerful solution with 60-80% performance depending on the tasks, the RTX A5000 is a more accessible option and could be an optimal choice for certain tasks (such as working with models like 8x7B). For more exotic inference solutions, you can consider cards like AMD Alveo™ V70, NVIDIA A2/L4 Tensor Core, and Qualcomm® Cloud AI 100. In the near future, AMD and NVIDIA plan to outperform Intel's GPU Gaudi 3 in the AI training market. Considering all these factors and taking into account software optimization for HPC and AI, we recommend servers with Intel Xeon or AMD Epyc processors and GPUs from NVIDIA. For AI inferencing tasks, you can use GPUs from RTX A4000/A5000 to RTX 3090, while for training and working on multi-modal neural networks, it's advisable to allocate budgets for solutions from RTX 4090 to A100/H100. Rent GPU servers with instant deployment or a server with a custom configuration with professional-grade NVIDIA Tesla H100 / H100 80Gb or RTX A5000 / A4000 cards. GPU servers with game RTX4090 cards are also available. Rent GPU servers with instant deployment or a server with a custom configuration with professional-grade NVIDIA Tesla H100 / H100 80Gb or RTX A5000 / A4000 cards. GPU servers with game RTX4090 cards are also available. Rent GPU servers with instant deployment Rent GPU servers with instant deployment custom configuration custom configuration GPU servers with game RTX4090 cards GPU servers with game RTX4090 cards This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

How Do You Choose the Best Server, CPU, and GPU for Your AI?

কিভাবে আপনি আপনার AI এর জন্য সেরা সার্ভার, CPU এবং GPU চয়ন করবেন?

Wie wählen Sie den besten Server, die beste CPU und die beste GPU für Ihre KI aus?

¿Cómo se elige el mejor servidor, CPU y GPU para su IA?

Comment choisir le meilleur serveur, CPU et GPU pour votre IA ?

आप अपने AI के लिए सर्वोत्तम सर्वर, CPU और GPU का चयन कैसे करते हैं?

AI に最適なサーバー、CPU、GPU をどのように選択すればよいでしょうか?

AI에 가장 적합한 서버, CPU 및 GPU를 어떻게 선택합니까?

Como você escolhe o melhor servidor, CPU e GPU para sua IA?

Как выбрать лучший сервер, процессор и графический процессор для вашего ИИ?

Yapay Zekanız için En İyi Sunucuyu, CPU'yu ve GPU'yu Nasıl Seçersiniz?

Làm cách nào để bạn chọn Máy chủ, CPU và GPU tốt nhất cho AI của mình?

如何为您的 AI 选择最佳的服务器、CPU 和 GPU？

TLDR: Ergo (ERG), launched in 2019, uses the unique Autolykos Proof of Work (PoW) consensus protocol to be ASIC-resistant. The article outlines the process of setting up Ergo mining, which involves selecting a suitable GPU, building a mining rig, creating an Ergo wallet, joining a mining pool, and choosing mining software. It also provides an overview of the Ergo tokenomics, network stats, and details on the factors that affect mining rewards. Factors that affect Ergo mining rewards include initial setup costs, electricity costs, ERG price volatility, software and hardware configuration, and hardware maintenance and repair costs. Based on a hypothetical scenario, Ergo mining is profitable if electricity costs are below $0.08/kWh. Even then, there may be doubts if it’s worthwhile. Therefore aspiring crypto miners are urged to thoroughly consider all factors before proceeding. Introduction to Ergo Mining This article briefly introduces you to the Ergo (ERG) cryptocurrency. Next, the article will guide you through the Ergo coin mining process. The article assumes you know the of cryptocurrencies, , and how crypto mining works. basics blockchains Ergo(ERG) is a cryptocurrency launched in 2019, with no ICO, pre-mined tokens, or pre-allocated tokens to team members and VCs. According to Ergo’s , every ERG token is protected by the Sigma cryptographic protocol and uses a scripting language called ErgoScript. Using ErgoScript enables users to create smart contracts on the Ergo blockchain. whitepaper Ergo uses its own unique Autolykos Proof of Work (PoW) consensus protocol to verify transactions on its blockchain. Moreover, the protocol ensures Autolykos ASIC miner resistance. Autolykos ASIC miner resistance is important because huge miners dominate blockchain networks like Bitcoin. These miners have access to expensive ASIC mining machines, making mining unviable for the average person. Therefore, making the Ergo blockchain ASIC-resistant with the Autolykos mining protocol is important to make mining more affordable. A Brief Overview of Ergo Tokenomics and Network Stats Before embarking on a cryptocurrency mining journey, you must understand its . As of the time of writing, the circulating supply of ERG tokens is around 71.15 million tokens and has a market cap of around $90.5 million. tokenomics Next, the network currently handles about 5994 transactions daily and takes 120 seconds on average to add a new block of transactions. Moreover, each ERG block successfully mined rewards with a min payout of 39 ERG tokens. Finally, the blockchain possesses computational power or a hashrate of 19.39 TH/s. How to Mine the Ergo Crypto When learning how to mine Ergo, we start by building a GPU mining rig and setting up an Ergo wallet. Next, we configure the hardware and software for the Ergo mining rig. Finally, we connect the Ergo miners to a mining pool. Choose a GPU As Ergo is ASIC-resistant, you must use a graphics card’s GPU to mine Ergo. Suitable GPUs must have a memory of more than 3GB. Popular graphics cards for mining Ergo include: Nvidia GTX 1060 GTX 1660 Ti RTX 3060 RTX 3060 Ti RTX 3070 RTX 3070 Ti RTX 3080 Ti RTX 3090 AMD RX 470 RX 580 Radeon 5700 XT RX 6600 XT RX 6800 RX 6800 XT While buying the cheapest GPU is tempting, you must consider the GPU’s cost versus its mining capabilities. The cheapest GPU may not be powerful enough to generate adequate mining rewards. However, the most expensive GPU may not be worth it for the rewards generated. LHR and GPU Mining Due to graphic card shortages, Nvidia GPUs are artificially limited in their crypto-mining capabilities. Hence, Light HashRate (LHR), was introduced in the latest Nvidia drivers to impose this limitation on Nvidia GPUs. The degree of LHR impact depends on the type of GPU memory and its manufacturer. When choosing a GPU, t have shown that Nvidia GPUs with GDDR6 Hynix memory are the most affected by LHR. Therefore, keep this in mind when choosing a suitable Ergo mining GPU. ests Building the Best Ergo Miner After choosing your GPUs, you must (basically a PC) to house the GPUs for crypto mining. build a mining rig Install Windows or Linux as the mining rig’s operating system. Once your mining rig is complete, give it a name, known as a , and Rig ID note it down. Create an Ergo Wallet You need an Ergo wallet to receive your mining rewards. However, some use , like Ergo mining Binance addresses, to cash out quickly and regularly. centralized exchange addresses If you want to hold ERG longer, it’s highly recommended that you use , such as the official Ergo wallet app. Once you have your , Ergo wallets wallet address note it down for later use. If you’re considering using a hardware wallet instead, you must note that hardware wallets have when used for mining purposes. issues Joining a Mining Pool Next, you want to join a mining pool. Although solo mining allows you to keep all block rewards, you might go for weeks or months without finding a block to successfully mine. Joining a mining pool allows you to pool your computing resources with other miners to increase everyone’s chances of getting block rewards. Mining pool block rewards will be distributed across the mining pool, and everyone will receive a smaller reward. However, rewards are more likely to be received in a mining pool. Therefore, the average crypto miner benefits by joining a mining pool as they earn smaller mining rewards more frequently. Popular Ergo coin include: mining pools Ergo pool 2miners Herominers Nanopool F2pool To join a mining pool, visit the mining pool’s website and the For example, if we want to use 2miner‘s Ergo USA server, we would note down the address: note down mining pool’s server address. us-erg.2miners.com:8888 Some mining pools require registering a free account. Therefore, check each mining pool’s website and follow their instructions. Choosing Your Mining Software The mining rig requires mining software to begin mining Ergo coins. Installing the best mining software depends on the type of GPU and operating system used in your mining rig. The top mining software for Ergo miners are: NBminer Team Red Miner SRB Miner (Has an LHR unlocker to mitigate some LHR impact) T-Rex Miner lolMiner NBminer is one of the popular Ergo mining software and reviews have shown that it’s good for Nvidia GPUs, while SRB Miner is good for AMD GPUs. Mining Software Configuration Guide First, download your chosen mining software archive and extract it. You may have to whitelist the software in your antivirus program, as the antivirus may flag the mining software as a malicious threat. After extracting, find a configuration file, usually a file or a file. Open the configuration file in a text editor like Notepad. .bat .cmd Recall your: Rig ID Ergo wallet address Mining pool’s server address. Copy and paste the three pieces of information into your mining software configuration file. Below is an example of how NBminer’s .bat configuration file looks like. nbminer.exe -a ergo -o stratum+tcp://erg.2miners.com:8888 -u YOUR_WALLET_ADDRESS.RIG_ID
pause In the example above, the is added before “-u.” The is after the “-u.” Finally, the is added behind the Ergo wallet address after a period. mining pool’s server address Ergo wallet address Rig ID Once you’re done, save the configuration file changes in Notepad. Take note that other mining software’s configuration files are not 100% similar but should follow a similar pattern. Operating System Tweaks If you’re using a Windows operating system, you must navigate to the Windows Power Settings in Control Panel and set it to Never Sleep. Next, turn off Windows Update in Control Panel. This is to ensure your miner is not interrupted while mining. Finally, ensure Window’s Virtual Memory, or pagefile, is set to at least 16 GB. Now you’re ready to run the mining software. Earn Ergo rewards by starting to mine today! How do I Check My Rewards? After 24 hours, you can check if your mining setup works by checking your wallet address in the . There, you can view transactions of mining rewards or the number of coins coming in. Ergo coin block explorer Moreover, your Ergo mining pool account dashboard provides a summary report for average mining rewards and hashrates. Factors Affecting Mining Rewards Finally, we arrived at the fun part. Most miners want to make a profit, so knowing how to estimate mining Ergo profit, is very important. Here are factors that affect your crypto mining profits. Initial Setup Costs The more initial outlay, the longer it takes to break even from mining. Apart from GPU costs, the remaining mining rig parts and the mining environment setup will also incur upfront costs. As crypto mining rigs produce a lot of heat and noise, crypto miners have to spend money to provide good cooling, ventilation, and soundproofing for their mining rigs. Electricity Costs The next factor is a huge one. Electricity costs are the number one factor that eats into profitability. Crypto mining in locations with high energy costs results in unprofitability. Furthermore, natural disasters or geopolitical conflicts could see energy costs soaring. We could see this happening in Europe during the start of the Ukrainian conflict. Some miners cut their electricity costs by accessing cheap renewable energy. For example, from oil production to power crypto mining cheaply is possible. using waste natural gas Apart from accessing cheap renewable energy, cryptocurrency miners often flock to locations with low energy costs. Countries with low average and decent infrastructure for crypto mining include: energy costs China at $0.08/kWh Malaysia at $0.047/kWh Qatar at $0.032/kWh Argentina at $0.034/kWh Ergo Coin Price Volatility As an asset class, cryptocurrencies are known to be volatile and high-risk. Ergo coin price is no exception to this. If Ergo’s price drops too low, mining could be unprofitable after factoring in running costs. Software and Hardware Configuration The computing power of GPUs largely determines how the number of coins you receive. You can configure a GPU to increase its computing power through a process known as “overclocking.” However, overclocking may risk potential system instabilities. Moreover, it produces additional heat, which reduces hardware reliability and lifespan. Additionally, overclocking increases electricity consumption. If the overclocking manages to be stable, you can maximize your GPU’s mining reward potential. Miners can also squeeze more profits through a hardware configuration called ”undervolting.” Undervolting makes a mining rig work with less electric power. Undervolting aims to boost mining reward efficiency by reducing electricity costs and decreasing heat generation. However, undervolting could risk introducing potential system instabilities and risk hardware damage. Finally, the type of mining software used on your hardware configuration could also mean the difference between more or less mining rewards. For example, mining software like NBminer works better on an Nvidia card than SRBminer. Hardware Maintenance Crypto mining rigs generate lots of heat and work for long periods of time under full loads. Therefore, any hardware maintenance and repairs will eat into mining profits. Is it worth it?: A Hypothetical Mining Scenario Before you start mining Ergo, you should use an online Ergo mining calculator, like the , to help you estimate your crypto-mining rewards. Whattomine Ergo mining calculator With entered hashrate, you can estimate mining rewards and profitability. Furthermore, this estimation can be more accurate by entering electric costs, current crypto prices, blockchain mining difficulty, and GPU specs. In a hypothetical Ergo mining scenario, we assume the to be $0.23/kWh. We also assume we have an AMD 5700 XT Ergo mining rig. average United States electricity cost In this scenario, we estimate “earning” negative $0.64 every 24 hours. This is due to the high electricity costs in the United States and the current prices of Ergo. However, by bringing down electricity costs to $0.08/kWh, you can break even. Therefore, assuming all other factors are constant, Ergo mining is only profitable if electricity costs are below $0.08/kWh Conclusion In conclusion, Ergo mining is an intriguing and educational venture for cryptocurrency enthusiasts. The Sigma cryptographic protocol protects the ERG token and relies on a unique Autolykos PoW consensus protocol, making the Ergo blockchain ASIC-resistant. Initial set-up costs and high electricity costs may challenge profitability. Additionally, the choice of mining hardware, mining pool choice, and hardware and software configurations all contribute to the efficiency of earning Ergo mining rewards. It’s worth noting that Ergo mining involves risks such as Ergo coin price volatility, volatile energy costs, and hardware or software failure. Ultimately, besides educational purposes, Ergo mining’s worth is doubtful, as it’s only profitable with electricity costs below $0.08/kWh. FAQ Is Ergo crypto mining ASIC resistant? Yes. The Autolykos protocol Ergo uses makes it ASIC-resistant, allowing you to use consumer GPUs for mining. Is Ergo mining affected by LHR? Yes, LHR affects Ergo mining. Mining with Nvidia GPUs using GDDR6 Hynix memory is the most impacted by LHR. The T-Rex mining software has an LHR unlocker to mitigate some LHR impact. However, the software only unlocks up to 80% of the hashrate on GDDR6 Hynix memory GPUs. Does an RTX 3060 Ti LHR affect mining Ergo and Ravencoin? LHR affects Ergo and Ravencoin mining. If the RTX 3060 Ti uses GDDR6 Hynix memory, the LHR impact is higher than non-GDDR6 Hynix memory GPUs. Can you use an Ergo mining CPU rig? Using a CPU to mine Ergo is possible but not recommended. When comparing hashrates and power consumption, a CPU is less efficient than a GPU to mine Ergo. You can mine Ergo with your CPU using mining software such as . SRBminer Can you use a GPU to mine Ergo? Yes. You must use mining software such as to use your GPU as an Ergo miner. NBminer What GPU specifications are needed for Ergo mining? Suitable Ergo mining GPUs must have memory exceeding 3GB. Ideally, if it's an Nvidia GPU, ensure that it doesn't have GDDR6 Hynix memory to reduce LHR’s impact. Is there an Ergo ASIC Miner? No. As Ergo uses the Autolykos ASIC miner resistant protocol, there are no Ergominer ASIC machines.

Is Ergo Mining Worth it? Uncovering Ergo's Mining Process

If you ever needed more proof that the world's richest man is no visionary, look no further than the events of last week. Having finally acquired Twitter, Elon Musk crowned himself the CEO of yet another company he personally did not build and followed it up with what can best be surmised as a.. bloodbath. 😞 Twitter laid off 3,700 people, or half of its workforce, on Friday — letting go of employees in teams considered important in maintaining advertisers' trust (content moderation) while those that remained were working round-the-clock to roll out new features on the platform. As former employees started flooding LinkedIn to say goodbyes and begin searching for new opportunities, Twitter asked some of them to return back to work because they were either fired mistakenly or were needed to build Musk's vision for the social media giant. Musk wants to make Twitter profitable but has yet to detail how he plans on doing so. Meetings with advertisers haven't gone as planned as tons of companies have paused ad spending to determine what to make of Twitter's new owner and air concerns over content moderation. In the interim, Musk wants to generate revenue by selling the verified blue check mark for $7.99 a month, making it easier for users sharing disinformation to appear more authentic while complicating Musk's own efforts to transform Twitter into a source of accurate information. As a side note, Musk seems to be taking a page straight out of Donald Trump's playbook, garnering free media attention for being so bizarrely eccentric that outlets have no choice but to spam our feeds with one news story related to Twitter after the next. Is there a term for Musk fatigue? Because we're surely feeling it. Twitter was trending #32 in this week's Tech Company Rankings. Layoffs Come to Silicon Valley ⛈️ Twitter wasn't the only tech company letting go of people this past week. Digital payment firm Stripe and ride-hailing company Lyft announced similar measures, though their rationale was more grounded in the state of the global economy than the whims of a billionaire who just purchased their company. Stripe said it would be letting go of around 1,000 people, or 14% of its workforce, in a move meant to help the company course correct after it reached new heights during the COVID-19 pandemic. Essentially, the company overhired in response to tripling its revenue and payment volume over the course of 2020 and 2021, perhaps hoping that this growth would continue. However, the winds are now blowing in the opposite direction, with "stubborn inflation, energy shocks, [and] reduced investment budgets" prompting CEO Patrick Collison to trim the fat around corners in an attempt to save costs. Lyft too is looking to become leaner given "several challenges playing out across the economy." In a letter to employees, the company's management blamed a looming recession and the "realities of inflation and a slowing economy" for its decision to cut 683 jobs, or 13% of its workforce, while selling its first-party vehicle service business. If that wasn't enough, Amazon told employees last week that it would no longer be hiring more people in its corporate workforce, citing the same macroeconomic reasons as Stripe and Lyft. Amazon ranked #11 in this week's Tech Company Rankings. AMD Brings Out Spanking New GPUs 🖥️ The GPU wars are heating up now that AMD has announced the 7 series XT and XTX graphic cards. By being cheaper and performing as well as their green counterparts, preliminary response to the GPUs have been overwhelmingly positive, even though tech reviewers are yet to benchmark the cards. Still, that didn't stop Linus Tech Tips from extrapolating the cards' performance, concluding that the beefier of the two newly announced AMD cards (the 7900XTX) performed just as well as Nvidia's 4090 at $600 less. It's not just about the performance though: the AMD card is smaller, consumes less power, and is less likely to be a fire hazard. It's been less than a decade since Lisa Su took over the reins at AMD, and already the company has disrupted the monopolies of titans Intel and Nvidia. It was only in 2017 that AMD announced the Ryzen line of processors, only to follow it up with one impressive product launch after the next and capture more and more market share in both the consumer and commercial markets. Nvidia ranked #44 in this week's Tech Company Rankings. In Other News.. Ajit Mohan left Facebook parent Meta's India operations to join rival Snap. 📸
Binance is said to have processed nearly $8 billion in cryptocurrency transactions from Iran despite U.S. sanctions on the country. 🪙
Twitter alternative Mastodon is seeing more and more users join its platform. 🐮 And that’s a wrap! Thanks for reading Tech Company News Brief Issue #23! See y’all next week. PEACE ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon If you ever needed more proof that the world's richest man is no visionary, look no further than the events of last week. Having finally acquired Twitter , Elon Musk crowned himself the CEO of yet another company he personally did not build and followed it up with what can best be surmised as a.. bloodbath. 😞 Twitter Twitter Twitter laid off 3,700 people, or half of its workforce, on Friday — letting go of employees in teams considered important in maintaining advertisers' trust (content moderation) while those that remained were working round-the-clock to roll out new features on the platform. As former employees started flooding LinkedIn to say goodbyes and begin searching for new opportunities, Twitter asked some of them to return back to work because they were either fired mistakenly or were needed to build Musk's vision for the social media giant. teams teams working round-the-clock working round-the-clock some of them to return back to work some of them to return back to work Musk wants to make Twitter profitable but has yet to detail how he plans on doing so. Meetings with advertisers haven't gone as planned as tons of companies have paused ad spending to determine what to make of Twitter's new owner and air concerns over content moderation. paused ad spending paused ad spending air concerns air concerns In the interim, Musk wants to generate revenue by selling the verified blue check mark for $7.99 a month , making it easier for users sharing disinformation to appear more authentic while complicating Musk's own efforts to transform Twitter into a source of accurate information . $7.99 a month $7.99 a month sharing disinformation sharing disinformation source of accurate information source of accurate information As a side note, Musk seems to be taking a page straight out of Donald Trump's playbook, garnering free media attention for being so bizarrely eccentric that outlets have no choice but to spam our feeds with one news story related to Twitter after the next. Is there a term for Musk fatigue? Because we're surely feeling it. Twitter was trending #32 in this week's Tech Company Rankings . Tech Company Rankings Tech Company Rankings Layoffs Come to Silicon Valley ⛈️ Layoffs Come to Silicon Valley Twitter wasn't the only tech company letting go of people this past week. Digital payment firm Stripe and ride-hailing company Lyft announced similar measures, though their rationale was more grounded in the state of the global economy than the whims of a billionaire who just purchased their company. Stripe Lyft Stripe said it would be letting go of around 1,000 people, or 14% of its workforce, in a move meant to help the company course correct after it reached new heights during the COVID-19 pandemic. Essentially, the company overhired in response to tripling its revenue and payment volume over the course of 2020 and 2021, perhaps hoping that this growth would continue. However, the winds are now blowing in the opposite direction, with "stubborn inflation, energy shocks, [and] reduced investment budgets" prompting CEO Patrick Collison to trim the fat around corners in an attempt to save costs. said said Lyft too is looking to become leaner given "several challenges playing out across the economy." In a letter to employees, the company's management blamed a looming recession and the "realities of inflation and a slowing economy" for its decision to cut 683 jobs, or 13% of its workforce, while selling its first-party vehicle service business. letter letter If that wasn't enough, Amazon told employees last week that it would no longer be hiring more people in its corporate workforce, citing the same macroeconomic reasons as Stripe and Lyft. Amazon citing citing Amazon ranked #11 in this week's Tech Company Rankings. AMD Brings Out Spanking New GPUs 🖥️ AMD Brings Out Spanking New GPUs The GPU wars are heating up now that AMD has announced the 7 series XT and XTX graphic cards. By being cheaper and performing as well as their green counterparts, preliminary response to the GPUs have been overwhelmingly positive, even though tech reviewers are yet to benchmark the cards. GPU wars GPU wars AMD Still, that didn't stop Linus Tech Tips from extrapolating the cards' performance, concluding that the beefier of the two newly announced AMD cards (the 7900XTX) performed just as well as Nvidia 's 4090 at $600 less. It's not just about the performance though: the AMD card is smaller, consumes less power, and is less likely to be a fire hazard. concluding concluding Nvidia Nvidia less likely less likely It's been less than a decade since Lisa Su took over the reins at AMD, and already the company has disrupted the monopolies of titans Intel and Nvidia. It was only in 2017 that AMD announced the Ryzen line of processors, only to follow it up with one impressive product launch after the next and capture more and more market share in both the consumer and commercial markets. disrupted disrupted Intel Nvidia ranked #44 in this week's Tech Company Rankings. In Other News.. In Other News.. Ajit Mohan left Facebook parent Meta's India operations to join rival Snap. 📸 Binance is said to have processed nearly $8 billion in cryptocurrency transactions from Iran despite U.S. sanctions on the country. 🪙 Twitter alternative Mastodon is seeing more and more users join its platform. 🐮 Ajit Mohan left Facebook parent Meta 's India operations to join rival Snap. 📸 left left Facebook Facebook Meta Meta Binance is said to have processed nearly $8 billion in cryptocurrency transactions from Iran despite U.S. sanctions on the country. 🪙 Binance Binance processed processed Twitter alternative Mastodon is seeing more and more users join its platform. 🐮 join join And that’s a wrap! Thanks for reading Tech Company News Brief Issue #23! See y’all next week. PEACE ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon

Musk Is No Visionary: Half of Twitter Workforce Laid Off

Musk no es un visionario: la mitad de la fuerza laboral de Twitter fue despedida

Musk n'est pas un visionnaire : la moitié de la main-d'œuvre de Twitter licenciée

मस्क इज़ नो विजनरी: आधे ट्विटर वर्कफोर्स की छंटनी की गई

Musk はビジョナリーではない: Twitter 従業員の半数が解雇される

Musk não é visionário: metade da força de trabalho do Twitter é demitida

Musk không có tầm nhìn xa: Một nửa lực lượng lao động trên Twitter đã sa thải

马斯克没有远见：推特一半员工下岗

A good GPU not only provides a , it effectively takes some load off from the CPU and increases the overall performance of 3D applications and software you use. Another sector strong GPUs are flourishing in is the crypto mining sphere. GPUs have now started being used for mining due to their efficiency over CPUs. richer gaming experience In this article, we’ll take a detailed look at the top 5 GPUs you should consider buying due to their crypto-mining capabilities. GPUs for Crypto Mining: Nvidia GeForce RTX 3080 AMD Radeon RX 6800 XT Nvidia GeForce RTX 3090 Nvidia CMP 40HX AMD Radeon RX 5700 XT After introducing each GPU, we will explain: Which GPU is best for crypto mining Which GPU is best for gaming 1. Nvidia GeForce RTX 3080 Starting at just $699, Nvidia’s GeForce RTX 3080 contains the new and improved . Crafted with 54 billion transistors, the Ampere architecture is a technology that accelerates AI adoption. The architecture also brings upgraded tensor cores for Deep Learning Super Sampling (DLSS), tapping into groundbreaking AI to boost frame rates while generating excellent, crisp game images. Ampere architecture By extending the power of tensor cores, Ampere architecture allows you plenty of room to crank up your gaming resolutions to the max for a stunning visual experience. The RTX 3080 brings 10GB of GDDR6X graphics RAM, 320-bit memory interface width, memory speed of 19,000 MHz and a maximum digital resolution of 7680x4320, allowing it to provide a splendid gaming experience while delivering a highly improved performance in comparison to its predecessor, the RTX 2080. Image: Nvidia Furthermore, Nvidia GeForce RTX 3080 features , simulating the physical behavior of light to bring real-time and cinematic-quality rendering to even the most visually intense games. The best thing about the ray tracing feature in this card is that due to DLSS technology’s video rendering techniques, it doesn’t sacrifice the frame rate while maintaining amazing video quality. It also introduces a new thermal-optimized design that promises 55% airflow, 30% more efficiency and the ability to be up to three times quieter during heated gaming sessions. ray tracing Although the RTX 3080 has limited potential for crypto mining, it is still one of the fastest GPUs for Ethereum mining, giving a baseline FE measurement of 85 MH/s. The performance can be further tuned to get around 93 MH/s, but it has to be noted that the processor can limit its mining performance unless modded with better thermal pads that lower memory temperatures. 2. AMD Radeon RX 6800 XT The AMD Radeon RX 6800 XT is AMD’s comeback to the high-end GPU market. Powered by Navi 21 graphics processor, it sports Red Team’s RDNA 2 graphics architecture based on a 7nm manufacturing process, allowing it to bring very powerful performance to the table. Image: AMD The card contains capability, guaranteeing support for ray tracing and variable-rate shading. With the Radeon RX 6800 XT, AMD has paired 16GB GDDR6 memory with a 256-bit memory interface, leading to smooth and high-quality gaming visuals at both 1080p and 4K. At $649, this kind of high video quality is truly astounding. DirectX 12 Ultimate Radeon’s image sharpening technique allows the RX 6800 XT to employ a contrast-adaptive sharpening algorithm that enhances in-game visual clarity and brings out clear details without impacting the overall game performance. The high-powered GPU also contains what Radeon refers to as the ‘ ’, a massive bandwidth amplifier that allows the cache to be seen by the entire graphics core, allowing for faster rendering during gameplay while reducing the power consumption. Infinity Cache Additionally, the RX 6800 XT comes with a memory speed of 16,000 MHz and a Boost Clock of 2250 MHz. These clocks let gamers enjoy a seamless gaming experience at high FPS levels for the majority of games. Like other top AMD graphic cards, the RX 6800 XT can be used to mine cryptocurrencies like Etherium, Beam, Expanse and Ubique, all at an amazing hash rate of 65 MH/s, with a power consumption of 135 watts. You can find its detailed mining hash rate details . here 3. Nvidia GeForce RTX 3090 The GeForce RTX 3090 claims to provide ‘the ultimate gaming experience.’ At a whopping $1500, you should fully expect it to. The ferocious GPU is stuffed with the same Ampere GA102 graphics processor as the GeForce RTX 3080, but it has more of everything - it contains 328 tensor cores, 82 ray tracing acceleration cores and 10,496 Compute Unified Device Architecture (CUDA) cores. These upgraded cores help with playing ray-traced games at 1440p and 4K resolution with utmost ease. The RTX 3090’s mammoth 24GB of GDDR6X memory capacity exceeds the , which, combined with its memory speed of 19,500 MHz, allows for a blistering gaming experience like no other. typically recommended 16GB At the time of its release, the RTX 3090 was the world’s first graphics card that enables you to play and watch games at , although other GPUs have followed suit since. At this level of gameplay, the detail is so crisp and clear that you’ll be able to easily discern individual blades of grass. Additionally, distant game elements such as avatars' skin wrinkles are instantly recognizable. It also contains , a bouquet of technologies designed to analyze and reduce input lag and system latency for a phenomenal gaming experience. 8K HDR Nvidia Reflex The high memory of RTX 3090, combined with the 10,496 CUDA cores and an astounding hash rate of 121 MH/s also makes it a top option for crypto mining - you might easily make it to three digits each month, depending on your power consumption. Image: Nvidia 4. Nvidia CMP 40HX CMP 40HX by Nvidia is a professional crypto mining focused graphic card. Based on architecture, Nvidia’s CMP 40HX comes equipped with the TU106 graphics processor, 2304 shading units and 256-bit bus width. The Turing architecture allows the card to function effectively with its 12 nm chipset. Turing With an introductory price of $699, the classy GPU features 8GB of GDDR6 VRAM and operates at a frequency of 1470 MHz, with a memory speed of 14 Gbps. Additionally, it features 144 texture mapping units and 64 render output units (ROPs). The card also contains DirectX Ultimate capability, leading to incredible graphics performance through variable rate shading for gaming. The CMP 30HX is optimized for Etherium crypto-mining performance. The card allows a fully open, airflow-optimized bracket and is configured to allow high mining efficiency. Its extremely durable ball bearings can withstand years of intense use, and remain virtually silent while spinning under load. The powerhouse is rated at 185 watts and adheres to a 36 MH/s hash rate. However, ASUS’s internal testing performance of eight CMP 40HX prototypes was revealed through an image by , and the cards were each pumping out over 43 MH/s, leading to even greater promise over the crypto mining performance of the CMP 40HX. VideoCardz 5. AMD Radeon RX 5700 XT The AMD Radeon RX 5700 XT is a high-end GPU built on the 7nm process and based on Navi 10 graphics processor. At just $450, it provides amazing value for money as well. AMD has paired the card with 8GB GDDR6 memory connected using a 256-bit interface, with a memory speed of 14 Gbps. The RX 5700 XT’s RDNA architecture allows incredible visual effects combined with reduced latency levels to make for a very responsive gaming experience. Its Displayport 1.4 data and audio connection supports up to 8K resolution at 60 Hz or 5K at 120 Hz, allowing a truly immersive experience. Image: AMD When it comes to crypto mining, the RX 5700 XT is right up there among the best. It has an average mining hash rate of , which is truly astounding for its price range. It is a great option for mining a variety of cryptocurrencies such as Ethereum, Monero and Ravencoin, among others. 54 MH/s The beast is equipped with a cooling system with an aluminum radiator, copper tubes and dispersion blades, designed specifically to increase airflow efficiency. This system allows it to maintain its performance consistency to a great extent during mining, withstanding high amounts of heat. Which GPU to buy in 2021? Since we have discussed all GPUs with regard to gaming as well as mining capabilities, we’ll divide this section into two parts, looking at both these factors. Which GPU should you buy for crypto mining? All the aforementioned GPUs contain strong mining capabilities in general. However, when they are pitted against each other, some major differences come to the fore. Although Nividia’s CMP 40HX is designed specifically for mining and has its strong points, it’s ultimately a low-end mining option by Nvidia, especially keeping in mind that the 50HX and upcoming 90HX blow it away in almost all aspects. The Nvidia RTX 3080, Radeon RX 6800 XT and RX 5700 are all strong contenders in their own right, with good hash rate levels and processing speeds. However, miners typically seek to mine for very long durations, and the possibility of the cards heating up is very real. There is a high probability that you’ll need to wait for a very long period of time to realize a good ROI with these options, if at all. That leaves the Nvidia RTX 3090 as the last GPU standing. Its incredibly high 24GB memory, coupled with the jaw-dropping hash rate of over 120 MH/s, make it an ideal option for miners, even for the longest durations. Yes, it is priced significantly higher than the other GPUs discussed in the article, but the main thing to keep in mind here is that if your priority is crypto mining, there genuinely is no better option than the RTX 3090. It’s understandable if you want to forego the powerhouse for less costly options in terms of gaming, but when it comes to mining, you should cough up more cash and get your hands on the RTX 3090. You’ll have the best possible shot at recouping your investment a few months down the line, so go for it! Nvidia also on limiting graphic cards capability for mining to keep the availability of its top hardware for gaming users. hinted Which GPU should you buy for gaming? The CMP 40HX is made primarily for mining, so we’ll not consider it here. Moreover, although the RX 5700 XT has good enough gaming specs, its primary strength also lies in the mining department. All the other three among Nvidia’s GeForce RTX 3080, GeForce RTX 3090 and AMD Radeon RX 6800 XT pack a real punch in terms of overall gaming performance, but when it comes to choosing which one to go for, various factors come into consideration. The Nvidia GeForce RTX 3090 graphic card is the clear winner when it comes to gaming, but there is a caveat - at $1500, it’s priced way higher than the other two. If you are a deep-pocketed enthusiast who doesn’t mind spending exuberantly for the absolute best performance possible - both gaming and general - then it’s the GPU to go for. But if price is your concern, you should narrow the options down to the other two. Even though Nvidia GeForce RTX 3080 is priced $50 higher than AMD Radeon RX 6800 XT, its Ampere architecture brings more to the table in terms of performance improvements than the latter’s RDNA 2 architecture. Furthermore, RTX 3080’s DLSS technology significantly improves the image resolution of games with minimal impact on the FPS rate. As such, RX 6800 XT doesn’t offer a direct counterpart to DLSS. Image: TomsHardware From the 10-game DirectX Ray Tracing benchmark comparison at 1440p resolution above, we can see that RTX 3080 holds an edge over RX 6800 XT in all but two games - and . The overall average of 61.5 FPS across the 10 games is better than RX 6800Xt’s 45.8. Godfall Dirt 5 For all these reasons, you should shell out a little more cash and go with Nvidia’s GeForce RTX 3080 - the extra $50 shouldn’t be a reason to give up its impressive features and slightly better overall gaming performance. What are your thoughts? Feel free to let us know in the . Hackernoon Community

5 Best GPUs for Crypto Mining

Just like mobile users have Android and IOS and console gamers have Xbox and PlayStation, the computer geeks around the world like to compare Intel and AMD. The AMD processors have been getting ignored by the majority of the community for a long while now. This is because a few years ago, there used to be a significant gap in quality between AMD and Intel. However, over the years AMD has made its processors more reliable and powerful. Today, you can even find yourself an AMD Ryzen processor that performs better than its Intel counterpart at a more affordable price. With that being said, in this article, I’ve compiled together a list of the 7 best laptops with a Ryzen 7 processor. 1. ASUS TUF Gaming Laptop Image credit: Laptop Mag The is the perfect choice for every single budget gamer. You can get this laptop with either an Nvidia GTX 1660ti or an Nvidia RTX 2060 GPU, both of which are pretty capable and reliable options that can handle most latest games. The ASUS TUF Gaming Laptop has a powerful Ryzen 7 4800H, and a 144hz panel that is perfect for playing esport titles. This gaming laptop has an awesome build quality that almost seems unreal for the reasonable price. ASUS TUF gaming laptop 2. HP Omen Image source: GSMArena The is the perfect laptop for people who don’t just want to use their laptops for gaming. This laptop is a bit pricier than the previous option on the list, but it has a significantly better design. The HP Omen has been built with comfort and durability in mind, so this machine is perfectly usable for office work. HP Omen The HP Omen gaming laptop has an AMD Ryzen 7 4800H processor paired with Nvidia’s powerful 1660ti graphics card that can handle a lot of modern games at medium to high graphical settings. 3. Lenovo Legion 5 Image source: Beebom is another gaming laptop in this list that provides us with the amazing power of the AMD Ryzen 7 4800H processor. This laptop also has an Nvidia GTX 1660ti graphics card and a 144hz panel. While some would argue that the HP Omen has a better design, I still prefer the Lenovo Legion 5 laptop because of all the things that it offers at an extremely affordable price. The Lenovo Legion 5 The Lenovo Legion 5 has pretty minimal branding, so it can also be used in office-type environments. This list of best laptops with a Ryzen 7 processor definitely needed a minimalistic gaming option! 4. Acer Swift 3 Image credit: Laptop Mag Let’s also talk about some laptops that don’t really come with a beefy GPU and are only used of performing basic tasks and office work. is one of the most affordable Ryzen 7 laptops that you can find in the market right now. While the design of this thing is quite simplistic, the Acer Swift 3 is a pretty thin and portable laptop. The Acer Swift 3 In my personal opinion, this laptop is perfect for every student as it can perform all the everyday tasks pretty easily. 5. ASUS ZenBook Image credit: BC Group is one of my most favorite laptops of all time because of its unique and reliable design. Many variants of the ASUS ZenBook are available right now! As this post is about the 7 best Ryzen 7 laptops, we’ll be focusing on the AMD processor variant of this device. While the AMD variant of the ASUS ZenBook is quite powerful and reliable, it doesn’t come with an OLED display. Luckily, the other awesome features of this laptop have been preserved by ASUS, so overall, you can have a pretty premium experience. The ASUS ZenBook 6. Microsoft Surface Laptop 3 Image credit: Microsoft If you are just looking for something to perform your daily tasks and light office work on, then is the number one option for you. The Microsoft Surface laptop comes with an AMD Ryzen 7 Surface edition and 32 GBs of RAM. Sadly, this thing has no dedicated GPU, so you won’t be able to enjoy any games. Microsoft Surface Laptop The Microsoft Surface Laptop has a pretty minimalistic and simplistic design and durability is something that Microsoft has a paid a lot of attention to with this product. 7. ASUS ROG Zephyrus Image credit: CNET Now, let’s talk about one of the best gaming laptops that money can buy! This premium gaming laptop is a bit pricier when compared to other options on this list. However, it offers the best build quality and awesome gaming performance. comes with an 8-core Ryzen 7 processor that is perfect for gaming and all kinds of other heavy tasks. Considering the fact that this machine is a gaming beast, its design is pretty minimalistic. The ASUS ROG Zephyrus The ASUS Rog Zephyrus is a bit thicker than some other laptops on this list, so you should only go with this option if you love gaming.

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