Nvidia Promises 40x Hopper Performance in Blackwell Unveil at GTC 2025

Hello AI Enthusiasts! Welcome to the eleventh edition of "This Week in AI Engineering"! "This Week in AI Engineering" NVIDIA unveiled its Blackwell platform delivering 40x Hopper performance, Baidu's ERNIE 4.5 outperforms GPT-4o at 1% of the cost, Mistral Small 3.1 achieves leading benchmark scores with just 24B parameters, and Google's Gemini Robotics brings advanced AI to physical systems. Plus, we'll cover Microsoft's strategic pivot with MAI models and RA.Aid's autonomous coding framework, alongside must-know tools to make developing AI agents and apps easier. NVIDIA GTC 2025: Major AI Infrastructure and Model Advancements NVIDIA GTC 2025: Major AI Infrastructure and Model Advancements NVIDIA has unveiled significant AI infrastructure and model advancements at GTC 2025, setting the stage for the next generation of reasoning and agentic AI capabilities. The company's announcements span from next-generation hardware to advanced AI models for robotics and reasoning. GTC 2025 GTC 2025 Next-Generation AI Compute Platforms Next-Generation AI Compute Platforms Blackwell Production: The Blackwell platform is now in full production, delivering 40x the performance of Hopper for reasoning AI workloads Blackwell Ultra: Coming in H2 2025, enhancing training and test-time scaling inference for agentic AI, reasoning, and physical AI applications Vera Rubin: Next-generation GPU architecture announced, featuring NVL 144 systems with completely redesigned components arriving in H2 2026 Annual Roadmap Rhythm: Established regular cadence for infrastructure updates to help organizations plan AI investments Blackwell Production: The Blackwell platform is now in full production, delivering 40x the performance of Hopper for reasoning AI workloads Blackwell Production Blackwell Ultra: Coming in H2 2025, enhancing training and test-time scaling inference for agentic AI, reasoning, and physical AI applications Blackwell Ultra Vera Rubin: Next-generation GPU architecture announced, featuring NVL 144 systems with completely redesigned components arriving in H2 2026 Vera Rubin Annual Roadmap Rhythm: Established regular cadence for infrastructure updates to help organizations plan AI investments Annual Roadmap Rhythm AI Performance Enhancements AI Performance Enhancements AI Factory Efficiency: Blackwell NVL72 with Dynamo delivers 40x the AI factory performance of Hopper Photonics Integration: New Spectrum-X and Quantum-X silicon photonics networking switches provide 3.5x more power efficiency, 63x greater signal integrity, and 10x better network resiliency AI Factory Efficiency: Blackwell NVL72 with Dynamo delivers 40x the AI factory performance of Hopper AI Factory Efficiency Photonics Integration: New Spectrum-X and Quantum-X silicon photonics networking switches provide 3.5x more power efficiency, 63x greater signal integrity, and 10x better network resiliency Photonics Integration AI Software and Foundation Models AI Software and Foundation Models NVIDIA Dynamo: New open-source software for accelerating and scaling AI reasoning models in AI factories DGX Spark and DGX Station: Personal AI supercomputers powered by the Grace Blackwell platform for AI development Llama Nemotron: Open model family with reasoning capabilities designed for creating advanced AI agents NVIDIA Isaac GR00T N1: World's first open, fully customizable foundation model for generalized humanoid reasoning and skills NVIDIA Cosmos: New world foundation models for physical AI development with unprecedented control over world generation Newton Physics Engine: Open-source physics engine for robotics simulation, developed with Google DeepMind and Disney Research NVIDIA Dynamo: New open-source software for accelerating and scaling AI reasoning models in AI factories NVIDIA Dynamo DGX Spark and DGX Station: Personal AI supercomputers powered by the Grace Blackwell platform for AI development DGX Spark and DGX Station Llama Nemotron: Open model family with reasoning capabilities designed for creating advanced AI agents Llama Nemotron NVIDIA Isaac GR00T N1: World's first open, fully customizable foundation model for generalized humanoid reasoning and skills NVIDIA Isaac GR00T N1 NVIDIA Cosmos: New world foundation models for physical AI development with unprecedented control over world generation NVIDIA Cosmos Newton Physics Engine: Open-source physics engine for robotics simulation, developed with Google DeepMind and Disney Research Newton Physics Engine The company anticipates significant growth in AI computing demand driven by reasoning and agentic AI, with NVIDIA's CEO Jensen Huang estimating data center buildout to reach $1 trillion. These developments underscore NVIDIA's focus on three key AI infrastructures: cloud, enterprise, and robotics, with a complete stack for each domain.ocusing on the emotional and contextual elements that make human communication meaningful, addressing the "emotional flatness" problem that limits user engagement with current systems. ERNIE 4.5: Baidu's Multimodal Model Shows Strong Performance Against Leading LLMs ERNIE 4.5: Baidu's Multimodal Model Shows Strong Performance Against Leading LLMs Baidu has released ERNIE 4.5, a native multimodal model designed to process text, image, audio, and video content within a unified framework. This new model represents a significant advancement in Baidu's AI capabilities with strong performance across multiple benchmarks. ERNIE 4.5 ERNIE 4.5 Multimodal Architecture Multimodal Architecture Joint Modeling System: Integrates multiple modalities through collaborative optimization Spatiotemporal Representation Compression: Enhances processing of temporal and spatial data Heterogeneous Multimodal MoE: Leverages mixture-of-experts architecture that activates specialized components only when needed Knowledge-Centric Training: Utilizes improved data construction methods for better understanding Joint Modeling System: Integrates multiple modalities through collaborative optimization Joint Modeling System Spatiotemporal Representation Compression: Enhances processing of temporal and spatial data Spatiotemporal Representation Compression Heterogeneous Multimodal MoE: Leverages mixture-of-experts architecture that activates specialized components only when needed Heterogeneous Multimodal MoE Knowledge-Centric Training: Utilizes improved data construction methods for better understanding Knowledge-Centric Training Performance Metrics Performance Metrics Average Score: 79.6 points across standard benchmarks, outperforming GPT-4o (69.8) and DeepSeek-V3 (79.14) Chinese Benchmarks: Superior results on C-Eval, CMMLU, and Chinese SimpleQA compared to non-Chinese models Reasoning Tasks: 94.1% on GSM8K mathematical reasoning benchmark, exceeding both GPT-4o and GPT-4.5 Deployment Cost: Operates at approximately 1% of GPT-4.5's cost and half the deployment cost of DeepSeek-R1 Average Score: 79.6 points across standard benchmarks, outperforming GPT-4o (69.8) and DeepSeek-V3 (79.14) Average Score Chinese Benchmarks: Superior results on C-Eval, CMMLU, and Chinese SimpleQA compared to non-Chinese models Chinese Benchmarks Reasoning Tasks: 94.1% on GSM8K mathematical reasoning benchmark, exceeding both GPT-4o and GPT-4.5 Reasoning Tasks Deployment Cost: Operates at approximately 1% of GPT-4.5's cost and half the deployment cost of DeepSeek-R1 Deployment Cost Ecosystem Integration Ecosystem Integration ERNIE Bot: Now freely available to all users ahead of schedule Baidu Search: ERNIE 4.5 capabilities being integrated across Baidu's product line Qianfan Platform: Available through APIs on Baidu AI Cloud for enterprise users and developers ERNIE X1: Companion model focused specifically on reasoning-intensive tasks in finance, law, and data analysis ERNIE Bot: Now freely available to all users ahead of schedule ERNIE Bot Baidu Search: ERNIE 4.5 capabilities being integrated across Baidu's product line Baidu Search Qianfan Platform: Available through APIs on Baidu AI Cloud for enterprise users and developers Qianfan Platform ERNIE X1: Companion model focused specifically on reasoning-intensive tasks in finance, law, and data analysis ERNIE X1 While ERNIE 4.5 demonstrates leading performance in many areas, it does show limitations in some specialized benchmarks including GPQA (science questions) and LiveCodeBench (coding capabilities) where GPT-4.5 maintains an edge. Baidu has announced plans to release ERNIE 5 later in 2025 with enhanced multimodal capabilities. Mistral Small 3.1: 24B Model Outperforms Larger Competitors with Superior Speed Mistral Small 3.1: 24B Model Outperforms Larger Competitors with Superior Speed Mistral AI has released Mistral Small 3.1, a 24B parameter model that demonstrates exceptional performance across text reasoning, multimodal understanding, and long-context processing while maintaining significant speed advantages over competitors. Mistral Small 3.1 Mistral Small 3.1 Performance Metrics Performance Metrics Scientific Reasoning: Achieves 46.7% on GPQA Diamond benchmark, outperforming both Claude-3.5 Haiku and GPT-4o Mini General Knowledge: 80.7% on MMLU benchmark, surpassing both Gemma 3-it (27B) and GPT-4o Mini Multimodal Tasks: 73% on MM-MT-Bench, significantly ahead of larger models including GPT-4o Mini (65%) Long Context: Leading performance on RULER 32K (94%) and strong results on RULER 128K (81%) Latency: Just 10.8 milliseconds per token, 25% faster than its closest competitors Scientific Reasoning: Achieves 46.7% on GPQA Diamond benchmark, outperforming both Claude-3.5 Haiku and GPT-4o Mini Scientific Reasoning General Knowledge: 80.7% on MMLU benchmark, surpassing both Gemma 3-it (27B) and GPT-4o Mini General Knowledge Multimodal Tasks: 73% on MM-MT-Bench, significantly ahead of larger models including GPT-4o Mini (65%) Multimodal Tasks Long Context: Leading performance on RULER 32K (94%) and strong results on RULER 128K (81%) Long Context Latency: Just 10.8 milliseconds per token, 25% faster than its closest competitors Latency Technical Architecture Technical Architecture Parameter Efficiency: Delivers top-tier performance with only 24B parameters versus competitors' 27-32B Multimodal Processing: Integrated vision capabilities with strong performance on MathVista (68%) Context Window: Expanded to 128K tokens with maintained performance at longer contexts License Model: Released under Apache 2.0 for full commercial use Parameter Efficiency: Delivers top-tier performance with only 24B parameters versus competitors' 27-32B Parameter Efficiency Multimodal Processing: Integrated vision capabilities with strong performance on MathVista (68%) Multimodal Processing Context Window: Expanded to 128K tokens with maintained performance at longer contexts Context Window License Model: Released under Apache 2.0 for full commercial use License Model Deployment Options Deployment Options Speed Optimization: Achieves 150 tokens per second throughput on standard hardware Integration: Available through Hugging Face, Ollama, Kaggle, and major cloud providers Hardware Requirements: Runs efficiently on a single RTX 4090 or 32GB MacBook Speed Optimization: Achieves 150 tokens per second throughput on standard hardware Speed Optimization Integration: Available through Hugging Face, Ollama, Kaggle, and major cloud providers Integration Hardware Requirements: Runs efficiently on a single RTX 4090 or 32GB MacBook Hardware Requirements Mistral Small 3.1 demonstrates that smaller, carefully optimized models can outperform larger counterparts across a wide range of benchmarks while delivering superior inference speeds. The model's strong scientific reasoning capabilities (shown in its GPQA performance) coupled with excellent multimodal processing make it particularly well-suited for complex real-world applications requiring both speed and accuracy. Gemini Robotics: Google DeepMind Brings Advanced AI Models to Robotics Gemini Robotics: Google DeepMind Brings Advanced AI Models to Robotics Google DeepMind has introduced two new AI models based on Gemini 2.0 that bridge the gap between digital AI capabilities and physical robot embodiments. This development represents a significant advancement in enabling robots to perform complex real-world tasks with greater adaptability and precision. Google DeepMind Google DeepMind Gemini Robotics Model Family Gemini Robotics Model Family Gemini Robotics: An advanced vision-language-action (VLA) model built on Gemini 2.0 that adds physical actions as a new output modality Gemini Robotics-ER: Specialized model with enhanced spatial understanding and embodied reasoning (ER) for roboticists running their own controller programs Gemini Robotics: An advanced vision-language-action (VLA) model built on Gemini 2.0 that adds physical actions as a new output modality Gemini Robotics Gemini Robotics-ER: Specialized model with enhanced spatial understanding and embodied reasoning (ER) for roboticists running their own controller programs Gemini Robotics-ER Key Capabilities Key Capabilities Generality: More than doubles the performance on generalization benchmarks compared to state-of-the-art VLA models Interactivity: Understands conversational language instructions in multiple languages and adapts to environmental changes in real-time Dexterity: Performs precise manipulation tasks (origami folding, snack packing) requiring fine motor skills Multi-Embodiment Support: Trained primarily on bi-arm ALOHA 2 platform but adaptable to various robot types including Franka arms and Apptronik's Apollo humanoid robot Generality: More than doubles the performance on generalization benchmarks compared to state-of-the-art VLA models Generality Interactivity: Understands conversational language instructions in multiple languages and adapts to environmental changes in real-time Interactivity Dexterity: Performs precise manipulation tasks (origami folding, snack packing) requiring fine motor skills Dexterity Multi-Embodiment Support: Trained primarily on bi-arm ALOHA 2 platform but adaptable to various robot types including Franka arms and Apptronik's Apollo humanoid robot Multi-Embodiment Support Technical Advancements Technical Advancements Spatial Reasoning: Enhanced 3D detection and pointing abilities compared to standard Gemini 2.0 On-Demand Code Generation: Generates appropriate grasping strategies and safe motion trajectories based on visual input End-to-End Control: Achieves 2-3x success rate compared to Gemini 2.0 in comprehensive robotics tasks Spatial Reasoning: Enhanced 3D detection and pointing abilities compared to standard Gemini 2.0 Spatial Reasoning On-Demand Code Generation: Generates appropriate grasping strategies and safe motion trajectories based on visual input On-Demand Code Generation End-to-End Control: Achieves 2-3x success rate compared to Gemini 2.0 in comprehensive robotics tasks End-to-End Control Safety Implementation Safety Implementation Layered Approach: Combines traditional robotics safety measures with AI-driven semantic understanding Safety Research: Released a new dataset for evaluating semantic safety in embodied AI Rule Framework: Developed data-driven "constitution" approach inspired by Asimov's Three Laws for safer robot behavior Layered Approach: Combines traditional robotics safety measures with AI-driven semantic understanding Layered Approach Safety Research: Released a new dataset for evaluating semantic safety in embodied AI Safety Research Rule Framework: Developed data-driven "constitution" approach inspired by Asimov's Three Laws for safer robot behavior Rule Framework Google DeepMind is collaborating with Apptronik to develop humanoid robots powered by Gemini 2.0, and has opened Gemini Robotics-ER to trusted testers including Agile Robots, Agility Robots, Boston Dynamics, and Enchanted Tools to explore real-world applications of these advanced models. RA.Aid AI Coding Agent with Three-Stage Development Architecture RA.Aid AI Coding Agent with Three-Stage Development Architecture RA.Aid (pronounced "raid") has been released as a standalone coding agent designed to develop software autonomously through a structured research, planning, and implementation workflow. Built on LangGraph's agent-based task execution framework, the tool offers a comprehensive approach to handling complex development tasks. RA.Aid RA.Aid Three-Stage Architecture Three-Stage Architecture Research Stage: Analyzes codebases, gathers context, and researches solutions using web sources via Tavily API Planning Stage: Breaks down tasks into specific, actionable steps with detailed implementation plans Implementation Stage: Executes planned tasks, makes code changes, and runs necessary shell commands Research Stage: Analyzes codebases, gathers context, and researches solutions using web sources via Tavily API Research Stage Planning Stage: Breaks down tasks into specific, actionable steps with detailed implementation plans Planning Stage Implementation Stage: Executes planned tasks, makes code changes, and runs necessary shell commands Implementation Stage Technical Features Technical Features Multi-Model Support: Works with multiple AI providers including Anthropic, OpenAI, OpenRouter, DeepSeek, and Gemini Expert Reasoning: Can selectively use advanced reasoning models like OpenAI's o1 for complex debugging Human-in-the-Loop Mode: Optional interactive mode for assistance during task execution Web Research Capabilities: Automatically searches for best practices and solutions when needed Specialized Code Editing: Optional integration with aider via the --use-aider flag Multi-Model Support: Works with multiple AI providers including Anthropic, OpenAI, OpenRouter, DeepSeek, and Gemini Multi-Model Support Expert Reasoning: Can selectively use advanced reasoning models like OpenAI's o1 for complex debugging Expert Reasoning Human-in-the-Loop Mode: Optional interactive mode for assistance during task execution Human-in-the-Loop Mode Web Research Capabilities: Automatically searches for best practices and solutions when needed Web Research Capabilities Specialized Code Editing: Optional integration with aider via the --use-aider flag Specialized Code Editing Deployment Options Deployment Options Default Mode: Basic coding tasks with confirmation prompts for shell commands Cowboy Mode: Skips confirmation prompts for automated execution in CI/CD pipelines Chat Mode: Interactive conversation about development tasks Server Mode: Web interface for team collaboration with real-time output streaming Default Mode: Basic coding tasks with confirmation prompts for shell commands Default Mode Cowboy Mode: Skips confirmation prompts for automated execution in CI/CD pipelines Cowboy Mode Chat Mode: Interactive conversation about development tasks Chat Mode Server Mode: Web interface for team collaboration with real-time output streaming Server Mode The tool is designed for both single-shot code edits and complex multi-step programming tasks that require deep codebase understanding. It can handle tasks ranging from explaining authentication flows to implementing new features and refactoring code across multiple files. RA.Aid is available for installation via pip (pip install ra-aid) and supports Windows, macOS, and Linux. The project is open source and accepts community contributions through GitHub. Microsoft MAI Models: New In-House AI Reasoning Models to Reduce OpenAI Dependency Microsoft MAI Models: New In-House AI Reasoning Models to Reduce OpenAI Dependency Microsoft is developing a new family of native AI reasoning models codenamed MAI (Microsoft AI) aimed at reducing its dependence on OpenAI while maintaining comparable performance to industry-leading models. This initiative represents a strategic pivot for Microsoft, which has invested approximately $13.75 billion in OpenAI since 2019. MAI Technical Architecture Technical Architecture Chain-of-Thought Reasoning: Models employ a human-like reasoning process that breaks down complex problems into intermediate steps Model Family: Multiple models being developed under the MAI umbrella, larger and more capable than Microsoft's earlier Phi models Benchmark Performance: Internal testing shows MAI models performing nearly as well as leading models from OpenAI and Anthropic Chain-of-Thought Reasoning: Models employ a human-like reasoning process that breaks down complex problems into intermediate steps Chain-of-Thought Reasoning Model Family: Multiple models being developed under the MAI umbrella, larger and more capable than Microsoft's earlier Phi models Model Family Benchmark Performance: Internal testing shows MAI models performing nearly as well as leading models from OpenAI and Anthropic Benchmark Performance Strategic Implementation Strategic Implementation Developer Release: Plans to release MAI as an API later in 2025 for third-party developers Copilot Integration: Already testing replacing OpenAI models with MAI in Microsoft 365 Copilot Multiple Provider Strategy: Testing models from xAI, Meta, and DeepSeek as potential OpenAI alternatives Developer Release: Plans to release MAI as an API later in 2025 for third-party developers Developer Release Copilot Integration: Already testing replacing OpenAI models with MAI in Microsoft 365 Copilot Copilot Integration Multiple Provider Strategy: Testing models from xAI, Meta, and DeepSeek as potential OpenAI alternatives Multiple Provider Strategy Market Positioning Market Positioning Cost Efficiency: Developing proprietary models to reduce recurring licensing fees for external AI Enhanced Transparency: Chain-of-thought reasoning provides clearer decision trails for enterprise users API Access: Will allow developers to embed MAI reasoning models into their own applications Cost Efficiency: Developing proprietary models to reduce recurring licensing fees for external AI Cost Efficiency Enhanced Transparency: Chain-of-thought reasoning provides clearer decision trails for enterprise users Enhanced Transparency API Access: Will allow developers to embed MAI reasoning models into their own applications API Access The initiative is led by Microsoft's AI division under Mustafa Suleyman, focusing on creating models that maintain performance while offering greater control over integration, cost structure, and technical roadmap. Despite this push for self-reliance, Microsoft is maintaining its relationship with OpenAI, with GPT-4 remaining an active component in Microsoft's current product portfolio. Tools & Releases YOU Should Know About Tools & Releases YOU Should Know About CodeWP is an AI-powered platform designed to simplify WordPress development. It offers AI chat and coding tools specifically trained for WordPress, enabling users to generate code snippets, troubleshoot issues, and even create entire plugins using natural language prompts. CodeWP is applicable for WordPress non-techies, WordPress developers, and WordPress agencies to enhance their WordPress workflow with AI. It caters to anyone from amateur developers to experienced professionals looking to streamline their processes and save time on WordPress-related tasks. **IBM watsonx Code Assistant for Z **is an AI-powered product designed to modernize mainframe applications. It helps developers understand, refactor, and optimize code, as well as convert COBOL to Java using generative AI. Applicable to businesses using IBM Z mainframes, it's particularly useful for application developers, IT architects, and modernization teams aiming to reduce costs, increase productivity, and streamline the modernization process, especially when onboarding new talent or creating RESTful APIs for their mainframes. Aider is a command-line tool leveraging OpenAI's models to function as an AI-assisted coding partner. It automatically generates code modifications and commits directly to Git repositories based on natural language instructions. Aider is technically suited for software developers, DevOps engineers, and technical project managers seeking to accelerate development cycles, automate repetitive coding tasks, and facilitate collaborative code generation. It is applicable in software development environments, version control systems, and CI/CD pipelines. Pixee.ai's Pixeebot is an automated code review tool that identifies security vulnerabilities and code quality defects. It generates pull requests containing suggested remediations, integrating directly into the development workflow via a GitHub app or CLI. Technically, it targets software developers and security engineers, automatically improving codebases and reducing the burden of manual code analysis by providing fixes ready for merging. It is applicable to any software development project hosted on GitHub, where automated code review and remediation are desired. CodeWP is an AI-powered platform designed to simplify WordPress development. It offers AI chat and coding tools specifically trained for WordPress, enabling users to generate code snippets, troubleshoot issues, and even create entire plugins using natural language prompts. CodeWP is applicable for WordPress non-techies, WordPress developers, and WordPress agencies to enhance their WordPress workflow with AI. It caters to anyone from amateur developers to experienced professionals looking to streamline their processes and save time on WordPress-related tasks. CodeWP CodeWP **IBM watsonx Code Assistant for Z **is an AI-powered product designed to modernize mainframe applications. It helps developers understand, refactor, and optimize code, as well as convert COBOL to Java using generative AI. Applicable to businesses using IBM Z mainframes, it's particularly useful for application developers, IT architects, and modernization teams aiming to reduce costs, increase productivity, and streamline the modernization process, especially when onboarding new talent or creating RESTful APIs for their mainframes. IBM watsonx Code Assistant for Z Aider is a command-line tool leveraging OpenAI's models to function as an AI-assisted coding partner. It automatically generates code modifications and commits directly to Git repositories based on natural language instructions. Aider is technically suited for software developers, DevOps engineers, and technical project managers seeking to accelerate development cycles, automate repetitive coding tasks, and facilitate collaborative code generation. It is applicable in software development environments, version control systems, and CI/CD pipelines. Aider Aider Pixee.ai's Pixeebot is an automated code review tool that identifies security vulnerabilities and code quality defects. It generates pull requests containing suggested remediations, integrating directly into the development workflow via a GitHub app or CLI. Technically, it targets software developers and security engineers, automatically improving codebases and reducing the burden of manual code analysis by providing fixes ready for merging. It is applicable to any software development project hosted on GitHub, where automated code review and remediation are desired. Pixee.ai Pixee.ai And that wraps up this issue of "This Week in AI Engineering." This Week in AI Engineering. Thank you for tuning in! Be sure to share this newsletter with your fellow AI enthusiasts and follow for more weekly updates. Until next time, happy building!