Ai Chip News & Updates

Amazon Web Services announced frontier agents, a new class of autonomous AI systems that can work without human intervention for hours or days, designed to automate complex software development tasks.

Nvidia has released Alpamayo-R1, an open reasoning vision language model for autonomous driving research, which can process both text and images together.

DeepSeek released two powerful AI models, DeepSeek-V3.2 and DeepSeek-V3.2-Speciale, matching the capabilities of GPT-5. These models can be used for free under an open-source MIT license.

AWS unveiled Trainium3, a new AI training chip with significant performance and efficiency improvements, and teased the upcoming Trainium4 chip that will support Nvidia's NVLink Fusion technology.

Mistral releases a family of 10 open-source AI models, including Mistral Large 3 and Ministral 3, designed to run on laptops, drones, and edge devices, prioritizing flexibility, efficiency, and distributed intelligence.

Arcee releases Trinity Mini and Trinity Nano Preview, two open-source AI models under Apache 2.0 license, aiming to regain U.S. control in open-source AI development.

Liquid AI releases a 51-page technical report detailing the architecture and training data behind its efficient on-device AI models, making them open-source for other organizations to use.

Nvidia and the University of Hong Kong release Orchestrator, an 8-billion-parameter model that coordinates tools and LLMs for complex problem-solving. This model outperforms larger models at a lower cost and improves efficiency.

AWS announced AI Factories, allowing businesses to run AI systems on-premises, addressing data sovereignty concerns and competing directly with Nvidia's offerings.

Amazon Bedrock has expanded its list of serverless models, adding 18 new open-weight models from various AI companies, including the new Mistral Large 3 and Ministral 3 models.

NVIDIA's Mistral 3 open model family offers industry-leading accuracy, efficiency, and customization capabilities for developers and enterprises.

NVIDIA and AWS expand their partnership to provide a secure, high-performance compute platform for AI innovation, featuring new technology integrations and custom AI infrastructure.

AWS and NVIDIA integrate to accelerate deployment for AI chips, Graviton CPUs, and other technologies via NVLink Fusion.

NVIDIA invested $2 billion in Synopsys, and OpenAI acquired an ownership stake in Thrive Holdings to accelerate enterprise AI adoption.

Databricks and NVIDIA enable enterprises to build production-grade AI systems for complex industry challenges with a joint platform combining Databrics Data Intelligence Platform, NVIDIA accelerated computing, and AI software stack.

NVIDIA has announced the winners of its Graduate Fellowship Program, awarding up to $60,000 to 10 PhD students for their AI research projects.

This article covers the global economy, markets, and finance, with various guests discussing the latest news and trends.

NVIDIA has introduced a new Game Ready driver that restores PhysX support for popular games such as Borderlands 2 and Mirror's Edge on RTX 50-series GPUs, but not all PhysX-accelerated games are supported yet.

Tracing Hanging and Complicated GPU Kernels Down To The Source Code: Developers can use user-induced GPU core dump generation, coupled with line information provided by compiling kernels with -lineinfo flag, to effectively identify and debug problematic GPU kernels.

NVIDIA Research introduces ToolOrchestra, a method for training small AI models to act as orchestrators, directing large models and tools with surgical precision to balance capability and cost.

NVIDIA unveiled open physical AI models, tools, and datasets at NeurIPS 2024, expanding research possibilities in AI development. Researchers can tap into open-source technologies, including Alpamayo-R1, the first open reasoning VLA model for autonomous driving.

Research suggests that the rapid progress of AI on consumer devices is limited by the constraints of Neural Processing Units (NPUs). Current NPU designs prioritize speed over efficiency and flexibility, making them less suitable for complex AI tasks. As a result, many AI applications run cloud-based systems, which have access to more resources and larger datasets. This limitation has significant implications for the development of edge AI, as it restricts the size and complexity of AI models that can be deployed on consumer devices.

Nvidia-backed startup Luma AI is launching a major expansion in London, hiring 200 employees, as it reaches a $4 billion valuation.

The NVIDIA Cosmos Cookbook provides a comprehensive guide to generating synthetic data for AI and machine learning tasks, including scalable data generation and domain adaptation.

Google's Gemini 3 model surpasses rivals in benchmark tests, prompting industry leaders to take notice, while Nvidia asserts its dominance in AI chips