A few months ago, I was looking at GPU memory usage on my laptop, wondering why my small language models kept running out of VRAM. Every AI researcher and engineer working with language models eventually hits this wall. You want to optimize for performance, but your hardware budget dictates otherwise. So I decided to dig deeper into memory usage and attention mechanisms in LLMs. I wasn't trying to build the next GPT-5, but had different ambitions: models that could actually run on regular hardware without sacrificing quality. The Memory Problem with LLMs The main memory bottleneck is the standard multi-head attention mechanism which creates massive key-value caches during inference. Every token you generate stores information that grows linearly with sequence length. For a 512-token context, you're looking at several MBs of cache data per token on a modest 30M parameter model. To find suitable alternatives, I started looking more into latent multi-head attention (MLA): a technique that compresses these key-value representations through learned projections. DeepSeek-V2 had shown great results with larger models, but surprisingly, nobody had tested it on small models where every parameter counts. Experimental Results I trained eight different configurations of GPT models on 100,000 synthetic stories from the TinyStories dataset: simple vocabulary, clear narrative structures which are perfect for isolating the effects of architectural changes. I expected MLA to help with memory. An unplanned surprise was how much rotary position embeddings (RoPE) would matter. Without rotary embeddings, MLA actually performed worse than standard attention: about 3-5% higher validation loss. Then I added RoPE to see if it could help offset the loss in quality. MLA+RoPE wasn't just matching standard attention. It was beating it by 2%. The optimal spot emerged at half-rank compression (r = d/2). The impact was as follows: 45% reduction in KV-cache memory usage1.4x inference speedupOnly 0.3% increase in validation loss (practically identical quality) 45% reduction in KV-cache memory usage 1.4x inference speedup Only 0.3% increase in validation loss (practically identical quality) I also pushed compression further: quarter-rank, eighth-rank, even thirty-second-rank. The models held together surprisingly well until around r = d/16, then fell off a cliff and started generating repetitive gibberish. Modeling Recipe MLA works by factorizing key and value projections through a bottleneck: instead of storing full K and V matrices, you store compressed latent representations and reconstruct them during attention computation. The compression ratio determines the trade-off between memory and quality. RoPE provides an important missing piece by encoding relative positions through rotation matrices, which helps models understand token relationships even with compressed attention and without explicit position embeddings. Applications Memory efficiency isn't only about fitting models on cheaper hardware. It also unlocks longer conversations, bigger batch sizes, and deployment scenarios that were previously difficult or impossible. We benchmarked this performance on NVIDIA A100s, but the implications also extend to mobile devices and edge computing. When every megabyte matters, architectural choices become as important if not more, than just raw parameter counts. I also ran the generated stories through GPT-4 for quality evaluation. Across grammar, creativity, narrative consistency: MLA+RoPE always scored highest across the board (7.4/10 overall vs 6.2/10 for standard attention). The automated scores matched our perplexity results almost perfectly. Lessons Learnt These smaller models themselves aren't revolutionary! Thirty million parameters won't replace GPT-5 anytime soon! But the architectural insights should also transfer  to larger models and real-world applications. We proved that small models can serve as effective testbeds for efficiency research. Looking Forward This research opens several paths worth exploring. Adaptive compression schemes that vary by layer or attention head could push efficiency further. Combining MLA with quantization or pruning techniques might compound the benefits. More importantly, we need to rethink the assumption that bigger is always better. In a world where AI needs to run everywhere (not just in data centers) architectural innovation matters as much as raw scale. For more details on the methodology and complete results, check out this paper on arXiv. You might also be interested in DeepSeek-V2's approach to MLA and the original RoPE paper that inspired our positional encoding strategy. For more details on the methodology and complete results, check out this paper on arXiv paper on arXiv . You might also be interested in DeepSeek-V2's approach to MLA DeepSeek-V2's approach to MLA and the original RoPE paper the original RoPE paper that inspired our positional encoding strategy.

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Optimizing LLM Pre-Training: Muon, Latent Attention, and MoE in Practice

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Google LLC is an American multinational corporation and technology company focusing on online advertising, search engine technology, cloud computing, computer software, quantum computing, e-commerce, consumer electronics, and artificial intelligence (AI).[9] It has been referred to as "the most powerful company in the world" by the BBC[10] and is one of the world's most valuable brands due to its market dominance, data collection, and technological advantages in the field of AI.[11][12][13] Alongside Amazon, Apple, Meta, and Microsoft, Google's parent company, Alphabet Inc. is one of the five Big Tech companies.

Google was founded on September 4, 1998, by American computer scientists Larry Page and Sergey Brin while they were PhD students at Stanford University in California. Together, they own about 14% of its publicly listed shares and control 56% of its stockholder voting power through super-voting stock. The company went public via an initial public offering (IPO) in 2004. In 2015, Google was reorganized as a wholly owned subsidiary of Alphabet Inc. Google is Alphabet's largest subsidiary and is a holding company for Alphabet's internet properties and interests. Sundar Pichai was appointed CEO of Google on October 24, 2015, replacing Larry Page, who became the CEO of Alphabet. On December 3, 2019, Pichai also became the CEO of Alphabet.[14]

The company has since rapidly grown to offer a multitude of products and services beyond Google Search, many of which hold dominant market positions. These products address a wide range of use cases, including email (Gmail), navigation and mapping (Waze, Maps and Earth), cloud computing (Cloud), web navigation (Chrome), video sharing (YouTube), productivity (Workspace), operating systems (Android), cloud storage (Drive), language translation (Translate), photo storage (Photos), videotelephony (Meet), smart home (Nest), smartphones (Pixel), wearable technology (Pixel Watch and Fitbit), music streaming (YouTube Music), video on demand (YouTube TV), AI (Google Assistant and Gemini), machine learning APIs (TensorFlow), AI chips (TPU), and more. Discontinued Google products include gaming (Stadia),[15] Glass, Google+, Reader, Play Music, Nexus, Hangouts, and Inbox by Gmail.[16][17] Google's other ventures outside of internet services and consumer electronics include quantum computing (Sycamore), self-driving cars (Waymo, formerly the Google Self-Driving Car Project), smart cities (Sidewalk Labs), and transformer models (Google DeepMind).[18]

Google Search and YouTube are the two most-visited websites worldwide followed by Facebook and Twitter (now known as X). Google is also the largest search engine, mapping and navigation application, email provider, office suite, online video platform, photo and cloud storage provider, mobile operating system, web browser, machine learning framework, and AI virtual assistant provider in the world as measured by market share.[19] On the list of most valuable brands, Google is ranked second by Forbes as of January 2022[20] and fourth by Interbrand as of February 2022.[21]

The company has received significant criticism involving issues such as privacy concerns, tax avoidance, censorship, search neutrality, antitrust and abuse of its monopoly position. On August 5, 2024, D.C. Circuit Court Judge Amit P. Mehta ruled that Google held an illegal monopoly over Internet search.[22]

Making LLMs Efficient: Reducing Memory Usage Without Breaking Quality

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