Dgx Spark and Strix Halo have very close specs and deliver similar performance. If nvidia makes their stack be more efficient with for example 50% more tockens on similar hardware specs agaist competitors, they don't need HF.
My take is that Chinese Labs, though slightly behind on the frontier (due to compute constraints) are on a trajectory to surpass Western labs (this is me speculating, reasons are better ecosystem creation on China's part and potentially better/more data environment). Qwen-3.5-122b was the king in it's category and noone came up with something better, even though many tried like poolside with laguna. Similar with 35b and 27b param models. I think poolside and HF acquisitions show us that nvidia really wants to have competitive models on the prosumer (~100-150b param size) and likely at the 300-500b as well. Together with a hardware to run them that's a good market to be in. And as the recently rumored Xiaomi AI cube shows us (together with gorgon/medusa halo and mac studios), this is a market segment that will have competition.
I do think it's a defensive move, but at first order it's defensive against OpenAI, Anthropic, and arguably Google. NVIDIA doesn't want to end up squeezed by those three as they increasingly make their own metal, etc.
Ensuring a healthy open-weight model market means ensuring continued demand for metal running in corporate data centers, bypassing any middleman. NVIDIA has a strong interest in a thriving "run your own agents" market.
They're actually aligned with Apple here. And in this market, Apple is a real competitor too, with Apple probably more consumer-centric, while NVIDIA probably more enterprise-centric.
That could be a factor, but the optimistic interpretation would be that they want to support the open ecosystem because it sells more chips.
Models are already largely hardware agnostic. It would be pretty hard to put that cat back in the bag.
I could imagine them building value-added services on top of HF to advantage Nvidia products (i.e. "run this model on NVIDIA cloud" with one-click), but in this moment it's hard to imagine how they could actively disadvantage models built to run on other platforms.
