What NVIDIA is
NVIDIA is a semiconductor and AI platform company best known for making the GPUs (graphics processing units) that power modern AI. Think of a GPU as a chip designed to do millions of calculations at the same time — exactly what you need when training or running a large AI model. Over the past few years, NVIDIA's hardware has gone from a tool for video games to the backbone of the global AI industry.
Why it matters to you
If you've used ChatGPT, Claude, Gemini, or almost any other AI product, there's a good chance NVIDIA hardware was involved somewhere in building or running it. The company sits at the center of the AI supply chain: labs need its chips, cloud providers buy them by the gigawatt, and even competing AI companies invest in NVIDIA stock.
The scale of recent deals makes this concrete. OpenAI and NVIDIA announced a partnership to deploy 10 gigawatts of AI datacenter capacity — enough to power a small city — with the first phase launching in 2026. Anthropic signed a deal for up to 1 gigawatt of NVIDIA's newest Grace Blackwell and Vera Rubin systems. NVIDIA also put $30 billion directly into OpenAI's latest funding round and up to $10 billion into Anthropic, making it a financial stakeholder in the companies that depend most on its hardware.
More than just chips
NVIDIA has been quietly building a full AI software and model ecosystem alongside its hardware business.
Software tools: NeMo is NVIDIA's framework for training and fine-tuning AI models. NIM (NVIDIA Inference Microservice) packages models into ready-to-deploy containers. TensorRT-LLM optimizes models to run faster on NVIDIA hardware. NemoClaw is an enterprise security and governance layer for AI agents, with launch partners including Salesforce, Cisco, and CrowdStrike.
Open-weights models: NVIDIA now publishes its own AI models that anyone can download and use. The Nemotron 3 family ranges from a massive 550-billion-parameter flagship (Nemotron 3 Ultra, the highest-scoring U.S. open-weights model on the Artificial Analysis Intelligence Index at the time of its release) down to a 4-billion-parameter model designed to run on a single device. These models use a hybrid architecture mixing traditional transformer layers with a newer design called Mamba, which makes them faster to run.
Physical AI: The Cosmos model family targets robots and physical-world applications — understanding video, predicting how objects move, and helping robots learn. Cosmos 3 was released as an open model for robotics developers, and Cosmos Reason 2 adds advanced reasoning for physical AI tasks.
AI for chip design: In a striking example of AI eating its own lunch, NVIDIA uses AI to design its own chips. Tools like NVCell (which redesigns thousands of circuit layout cells overnight, a job that used to take ten engineer-months) and PrefixRL (which produces arithmetic circuits 20–30% better than human designs) are already in use internally.
The open-weights strategy
NVIDIA committed to a $26 billion, five-year investment in open-weights models. The stated motivation is partly strategic: a healthy ecosystem of open models drives demand for AI hardware. There's also a competitive angle — Chinese AI labs have been building capable open-weights models on non-NVIDIA chips, and NVIDIA wants to ensure the best open models run on its hardware. The Nemotron Coalition, which NVIDIA formed with Mistral AI as a founding member, is one vehicle for this: combining Mistral's model-building expertise with NVIDIA's compute and data pipelines.
Navigating the competition
Not everyone is betting entirely on NVIDIA. OpenAI is co-developing custom AI accelerators with Broadcom targeting 10 gigawatts of capacity by 2029, and signed a separate 6-gigawatt deal with AMD. DeepSeek, a Chinese AI lab, gave Huawei early access to its upcoming V4 model for hardware optimization while reportedly denying the same to NVIDIA — a sign of deepening geopolitical tensions in the chip supply chain.
NVIDIA's response has been to deepen its relationships financially (investing billions in the labs it supplies), technically (co-optimizing future chip architectures with Anthropic), and through open-source goodwill (releasing models and datasets freely). The bet is that even as some customers build alternatives, NVIDIA remains too embedded — in hardware, software, and now equity — to be easily displaced.
Where things stand
NVIDIA is no longer just a chip company that happens to sell to AI labs. It is an investor, a model publisher, a software platform, and a research organization — all built on top of a hardware business that remains, for now, without a clear equal in the AI compute market.
