Nvidia is now giving away free AI models! Which is very good for selling Nvidia chips
&imwidth=600&imheight=450&format=webp&quality=medium "Nvidia is now giving away free AI models! Which is very good for selling Nvidia chips")
Nvidia is now giving away free AI models! Which is very good for selling Nvidia chips Photograph: (Pexels)
Nvidia has released Nemotron 3.5 Lightning, an open mixture-of-experts model with 30 billion total parameters but only 3 billion active per token, delivering up to four times the throughput of comparable open models. Built for the execution layer of long-running agents, it was pre-trained on over 20 trillion tokens and supports a one-million-token context window.
The company that sells the chips everyone needs to run AI models has started giving away the models. That is less generous than it sounds, and more strategic.
What Was Released
Nemotron 3.5 Lightning is an open mixture-of-experts model with 30 billion total parameters, of which only about 3 billion activate for any given token. It was pre-trained on more than 20 trillion tokens and supports context windows up to one million tokens.
The architecture is unusual. It interleaves Mamba-2 layers with mixture-of-experts layers alongside selected attention layers — a hybrid design rather than the standard transformer stack, chosen for throughput rather than raw reasoning capability.
The performance claim follows from that: up to four times the output speed of comparable open models of similar size, and roughly 30 per cent lower task completion time. Nvidia also open-sourced NeMo Switchyard, a model router that directs requests to whichever model suits them.
What It Is Actually For
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Nvidia is explicit that this is not a frontier reasoning model, and that clarity is worth noting in a market where every release claims to be the best at everything.
Nemotron 3.5 Lightning is built for the execution layer of long-running agents — the high-volume, repetitive, specialised steps an autonomous system performs constantly while working through a task. An agent might use a powerful model to plan and then call something like Lightning thousands of times to carry out individual steps.
For that job, speed and cost matter far more than sophistication. Activating 3 billion parameters instead of 30 billion is what makes running an agent continuously economically viable rather than ruinous.
The Strategy Underneath
Nvidia is not in the business of selling model access. It sells the hardware models run on. Those are different businesses with opposite incentives.
For OpenAI or Anthropic, a capable free model is a competitive threat — it undercuts what they charge for. For Nvidia, a capable free model is demand generation. Every developer who deploys an open model needs GPUs to run it. Every agent making thousands of calls consumes compute. The cheaper and faster the model, the more of it people run, and the more hardware that requires.
It is the same logic that has driven Nvidia's other recent moves. It committed $5 billion to Safe Superintelligence in a deal that returns as hardware purchases. It participated in Australian infrastructure startup Firmus's $2 billion round. And this week it partnered with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to assemble more than $500 billion in financing for AI infrastructure, using compute itself as collateral.
Financing the buyers, investing in the labs, and now supplying free models — every move points demand toward the one thing Nvidia actually sells.
The Competitive Effect
The release lands in the middle of a collapse in what AI capability costs.
Moonshot AI published Kimi K3's full weights free on July 27 — 2.8 trillion parameters, the largest open release in history. DeepSeek's V4 Flash came within two points of Claude Opus 4.7 on coding benchmarks at roughly one per cent of the price. Meta open-sourced Muse Glimmer this week, a 30-billion-parameter model that runs on a single consumer GPU, alongside a Zuckerberg manifesto arguing superintelligence should be distributed rather than held. OpenAI made ChatGPT unlimited and free for its billion weekly users.
Nvidia adding a fast, free, permissively available execution model to that mix pushes the floor lower again. For the laboratories trying to charge for model access, it is one more competitor giving away what they sell. For Nvidia, it is a customer acquisition cost — and one paid in software, which is considerably cheaper than paying it in silicon.
