What NVIDIA is
NVIDIA is a semiconductor and AI platform company — most famous for making the graphics processing units (GPUs) that power modern AI. If you've heard of ChatGPT, Claude, or Gemini, the computers those systems run on almost certainly contain NVIDIA chips. In the AI era, NVIDIA's hardware has become as fundamental as electricity: you can't build a frontier AI model without a lot of it.
But NVIDIA is no longer just a chip seller. It has evolved into a full-stack AI company — investing in the labs that use its hardware, building its own AI models, and developing software that makes its chips easier to use at scale.
Why it matters to you
Here's the simplest way to think about it: every time a major AI company announces a breakthrough, NVIDIA's hardware was almost certainly involved. OpenAI, Anthropic, Google, Mistral, Apple — they all rely on NVIDIA GPUs to train and run their models. That makes NVIDIA one of the most strategically important companies in technology today, even if its name doesn't appear on the products you use every day.
The chip business: everyone's landlord
The events in this bundle paint a clear picture of NVIDIA's central role. NVIDIA and OpenAI announced a partnership targeting 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 one gigawatt of NVIDIA's Grace Blackwell and Vera Rubin compute systems. Mistral AI built its Mistral 3 family on 3,000 NVIDIA H200 GPUs and co-optimized its models with NVIDIA's Blackwell chips. Even Mistral's sovereign cloud offering, Mistral Compute, runs on NVIDIA hardware.
When Anthropic needed to rapidly expand capacity — doubling Claude Code rate limits and removing peak-hour restrictions — it did so in part by accessing over 220,000 NVIDIA GPUs through a deal with SpaceX's Colossus data center.
NVIDIA as investor and partner
NVIDIA isn't just selling to AI labs — it's betting on them. The company invested $30 billion in OpenAI's massive funding round and up to $10 billion in Anthropic, making it a financial stakeholder in two of the most important AI companies in the world. It also participated in Mistral AI's €1.7 billion Series C and formed the NVIDIA Nemotron Coalition with Mistral to co-develop open-source frontier models.
These aren't passive investments. NVIDIA and Anthropic are actively co-optimizing model performance and future chip architectures for Anthropic's specific workloads — meaning the chips of tomorrow are being shaped by the AI models of today.
NVIDIA's own AI models
Perhaps the most surprising development: NVIDIA now builds and releases its own AI models. The Nemotron family covers a wide range:
- Nemotron 3 Ultra (550 billion parameters) is the largest, described as the highest-scoring U.S. open-weights model on the Artificial Analysis Intelligence Index — though it trails leading Chinese models.
- Nemotron 3 Super 120B is a hybrid architecture model that claims the fastest inference speed in its size class, at 442 tokens per second.
- Nemotron 3 Nano 4B targets on-device use cases.
- Audex 30B handles audio understanding, speech recognition, and text-to-speech in a single model.
- Nemotron 3 Embed ranked first on the RTEB benchmark for agentic retrieval tasks.
NVIDIA releases most of these models with open or permissive licenses — a strategic choice. As the company itself has framed it, a healthy open-weights ecosystem drives demand for AI semiconductors.
Cosmos: AI for the physical world
Beyond language models, NVIDIA released Cosmos 3, described as the first open omni-model for physical AI reasoning and action — meaning it's designed to help robots and autonomous systems understand and interact with the real world. A companion Cosmos 3 Edge variant targets deployment on edge devices. This positions NVIDIA at the frontier of embodied AI and robotics, not just text and code.
NVIDIA researchers also published RoboTTT, a robot training approach that extends how far into the past a robot can "remember" during a task — achieving an 87% performance improvement over single-step baselines and completing a five-minute, ten-stage assembly task for the first time.
AI designing NVIDIA's own chips
One of the more remarkable stories in this bundle: NVIDIA uses AI to design its own hardware. At GTC 2025, NVIDIA's chief scientist described tools like NVCell (which redesigns thousands of chip layout cells overnight, a job that would take human engineers months) and PrefixRL (which produces arithmetic circuits 20–30% better than human designs). The company also uses large language models fine-tuned on internal GPU documentation to help engineers find bugs and answer technical questions.
The competitive picture
NVIDIA's dominance isn't guaranteed. OpenAI is developing custom AI accelerators with Broadcom, targeting 10 gigawatts of capacity by 2029. OpenAI also struck a deal with AMD for 6 gigawatts of AMD GPUs. And in a geopolitically charged move, DeepSeek gave Chinese chipmaker Huawei early access to its V4 model for hardware optimization while excluding NVIDIA and AMD — a sign that AI supply chains are fracturing along national lines.
Where it's heading
The events in this bundle suggest NVIDIA is executing a strategy of deep entrenchment: be the hardware everyone needs, invest in the labs building on that hardware, build your own models to demonstrate what the hardware can do, and release those models openly to grow the ecosystem. The risks are real — custom silicon and geopolitical fragmentation could erode its position — but for now, NVIDIA sits at the center of nearly every major AI story being written.
