Nvidia’s reported $12.9 billion agreement to acquire Hugging Face sparked discussions among influencers on X, with many framing the deal as Nvidia securing the distribution layer for open-source AI models rather than buying a software business.
The surge in discussions focused on why the company is paying such a high price for a platform with $150 million in annual revenue, which influencers described as a bet on strategic control rather than immediate financial performance.
Influencers noted that this move allows Nvidia to own the primary hub where developers share models and serves as a way to keep the industry anchored to its hardware, says GlobalData, a leading intelligence and productivity platform.
Shreyasee Majumder, Social Media Analyst at GlobalData, said: “Influencers view the valuation as a defensive, ecosystem-level move rather than a conventional software deal.
“The high price was viewed less as a reflection of Hugging Face’s current revenue and more as a bet on preserving Nvidia’s long-term influence over the infrastructure and workflow layer of open AI.
“In the conversations on X, the deal was framed as a way for Nvidia to reinforce its position in the open-source AI stack by keeping inference and fine-tuning workflows tied to its CUDA ecosystem, even as major customers such as Google, Amazon, Microsoft, and OpenAI continue developing their own custom chips.”
Jen Zhu Scott, Co-founder and CEO of Power Dynamics said: “Hugging Face is the GitHub for open models. NVIDIA already sells the GPUs they run on. Owning the distribution layer (not just silicon) lets NVIDIA push CUDA / TensorRT / NIM / DGX Cloud + its own Nemotron as the path of least resistance. Closed labs (OpenAI, Anthropic) are exploring custom chips. Open-source inference + fine-tuning still run overwhelmingly on NVIDIA. Controlling the hub where those models live defends that demand. $13B is a high X on $150M ARR, but small relative to NVIDIA’s cash flow. The payoff is ecosystem control, not software revenue…..”
Jenny Xiao, General Partner at Leonis Capital said: “….Hugging Face is ownership. And it sits unusually far upstream in the deployment decision, where developers discover models, download weights, use libraries and runtimes, and connect them to hardware. That matters because Nvidia’s biggest customers are all trying to reduce their dependence on Nvidia.
“Google has TPUs. Amazon has Trainium. OpenAI has Jalapeño. Microsoft and Meta are building their own silicon too. ….”
Shakthi Vadakkepat, Enterprise AI Architect said: “….This is a chip-moat deal as much as a software deal. Closed labs are building their own silicon. Whoever owns the model catalog owns the first place those weights get optimized.
“Nvidia already poured money into open models. Buying the distribution layer keeps inference demand pointed at CUDA while it also gives Nvidia a path back into cloud after it pulled back on DGX Cloud.
“If it closes, this becomes Nvidia’s largest completed acquisition, bigger than the $7 billion Mellanox deal….”
Kim, Technology Expert said: “…Nvidia is paying for strategic control, not current sales. Strong open models help protect demand for its GPUs as OpenAI, Anthropic, Google and other major customers develop competing AI chips. Hugging Face could also revive Nvidia’s cloud ambitions by connecting millions of developers, models and workloads directly with Nvidia compute. Smart and huge deal.”
Gokul Rajaram, Founding Partner at Marathon Management Partners said: “…NVIDIA made three large acquisitions / investments in the space of a few days: Hugging Face, Poolside and Perplexity. (Two rumored, but seem likely) All three target the same goal: help build a robust open source ecosystem as a counterweight to OpenAI and Anthropic. Hugging Face is the distribution layer — the GitHub of open-source AI, where models, datasets and tooling live. Poolside is the model-building layer — NVIDIA is paying $6B to license its “Model Factory” technology, while investing another $1B in the company. The obvious application is accelerating NVIDIA’s own Nemotron/open-weight model efforts…”
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