Browsing Tag
NVIDIA
14 posts
NVIDIA chips, software, AI infrastructure, and developer platforms.
Claude Science Turns Research AI Into a Lab Workflow Layer
Anthropic’s Claude Science beta gives researchers an AI workbench for literature review, code, compute jobs, scientific figures, and lab-specific agents. The launch matters because it treats AI for science less like a single model race and more like a workflow layer that has to connect databases, HPC systems, NVIDIA BioNeMo tools, and reproducible artifacts.
Verkada and NVIDIA Push Physical AI Deeper Into Security Cameras
Verkada says NVIDIA is now both an investor and technical collaborator as it scales physical AI across more than 2.4 million devices. The deal turns enterprise security cameras into a clearer test case for AI video search, synthetic training data, and governance around real-world monitoring.
Etched’s $1B Sohu Backlog Turns AI Inference Into the Next Chip Fight
Etched says it has raised $800 million, signed more than $1 billion in customer contracts, and started production of its Sohu-based inference racks. The startup’s transformer-specialized chip is a serious bet that AI’s next hardware fight will be won on serving models, not just training them.
Nvidia’s Firmus Deal Turns Batam Into an AI Factory Test Case
Firmus will build a 360 MW Nvidia DSX AI factory campus in Batam, Indonesia, with access to as many as 170,000 Nvidia accelerators. The deal shows how AI infrastructure is shifting from one-off data centers toward financed cloud capacity for AI-native companies.
OpenAI’s Jalapeño Chip Puts Inference Costs at the Center of the AI Race
OpenAI and Broadcom unveiled Jalapeño, OpenAI’s first custom inference accelerator for large language models. The chip is less about replacing Nvidia overnight than controlling the cost, latency, and supply of the compute that runs products like ChatGPT, Codex, and the API.
Qualcomm’s Modular Deal Is a $3.9 Billion Bet on AI Software Portability
Qualcomm agreed to acquire Modular in a nearly $4 billion stock deal, giving its AI data center push a software layer built around portable model deployment. The move is aimed at a practical bottleneck in AI infrastructure: making models run efficiently across CPUs, GPUs, NPUs, and custom accelerators without locking developers into one hardware stack.
NVIDIA Rubin Pushes AI Data Centers Toward Hotter, Drier Cooling
NVIDIA says its Rubin-generation AI infrastructure can run fully liquid-cooled servers with 45°C coolant, cutting facility cooling water use from conventional tower-based levels to near zero in favorable climates. The design is a real shift for AI factories, but it does not erase the water tied to power generation, chip manufacturing, or local data center siting fights.
Groq’s $650M Raise Makes AI Inference the New Cloud Fight
Groq raised $650 million to expand its AI inference cloud, with 13 data centers, more than five million developers, NVIDIA LPX integration, and a 200 MW capacity target by the end of 2027. The deal shows why serving AI models is becoming its own infrastructure market, separate from the training race.
NVIDIA Halos Turns Robot Safety Into a Full-Stack AI Platform
NVIDIA Halos for Robotics gives robot makers a shared safety stack for physical AI, combining IGX Thor compute, Halos OS, sensor infrastructure, outside-in safety agents, and an inspection lab for certification. Agility Robotics is the first public adopter, bringing parts of the system into Digit humanoid deployments for factories, warehouses, and logistics operations.
Amazon’s Trainium Talks Push AWS Chips Beyond the Cloud
AWS is in early talks to sell Trainium AI chips for use in other companies’ data centers, a shift that could move Amazon from cloud-only accelerator provider toward a more direct role in the AI chip market. The opportunity is real, but so are the constraints: Trainium capacity is already tight, Nvidia still owns the broadest software ecosystem, and selling racks outside AWS could weaken the cloud bundle that makes custom silicon so valuable to Amazon.