Nvidia Commits About $7 Billion to Open-Weight Models Through a Poolside Licensing Deal

Nvidia has agreed to pay Poolside $6 billion for a non-exclusive licence to the startup's model-training technology, and to invest a further $1 billion at a $12 billion pre-money valuation. The deal was reported by The Wall Street Journal, summarized in Russian-language technical channels and confirmed in English coverage. About 109 Poolside engineers will move to Nvidia and join Nemotron, the company's family of downloadable open-weight models. Poolside's management team stays independent and continues its own research.

The stated purpose is competitive rather than scientific. Nvidia executives have framed the spending as an effort to build a United States open-weight stack that can compete with Chinese releases such as DeepSeek and Kimi. The aim, according to Seeking Alpha, is also to give enterprises a cheaper and modifiable alternative to the closed application programming interfaces (APIs) sold by OpenAI and Anthropic.

The starting position is modest. Nvidia's most recent open release, Nemotron 3.5 Lightning, is a 30-billion-parameter mixture-of-experts model with about 3 billion active parameters. It ships with post-training data and recipes and targets the high-volume mechanical layer of agent work: tool calls, retries, formatting and validation. Nvidia reports about 86 percent accuracy on PinchBench, an agentic benchmark of its own construction, and roughly 30 percent faster completion of 10,000 tasks than a comparable Qwen model. Those are vendor numbers on a vendor benchmark. Independent comparisons over the past month have generally placed Kimi K3 and DeepSeek V4 Pro ahead of the American open-weight entries on task coverage.

There is a clear conflict of interest here. Nvidia sells the accelerators that any serious open-weight training run consumes, and a larger supply of downloadable models expands on-premises and sovereign inference demand, the segment where Nvidia captures the most value per unit sold. Critics have argued the company is effectively underwriting demand for its own hardware. Both can be true at once: the deal buys Nvidia a training organization it did not have, and it also buys future GPU consumption.