Nemotron 3.5 Bolt E4B 4GB from @NvidiaAI is now available on Ollama. This new small open model is built for local assistants, interactive characters and robots that can carry on a conversation and use tools to get things done.

Nemotron 3.5 Super VL from @NVIDIAAI brings vision, reasoning & tool use to Deep Agents via ChatNVIDIA. Open weights. 120B parameters, 12B active. Nemotron 3.5 Super VL averaged an 87% pass rate on our Deep Agents behavioral suite. Speed stood out, too. On our Harbor-lite benchmark: 55% less total task time than DeepSeek V4.1 Flash, with similar success.

Nemotron 3.5 Lightning is free on AI Gateway with @NVIDIAAI. Open-weight 30B reasoning model with low latency, built for agents. 𝚗𝚟𝚒𝚍𝚒𝚊/𝚗𝚎𝚖𝚘𝚝𝚛𝚘𝚗-𝟹.𝟻-𝚕𝚒𝚐𝚑𝚝𝚗𝚒𝚗𝚐-𝚏𝚛𝚎𝚎

Nemotron 3.5 Lightning (30B-A3B) fits on a single 16 GB card. I ran it on my RTX 5060 Ti, every layer on the GPU. ggml-org's model GGUFs start at 17.6 GiB, unsloth's at 18.1 GiB. Myric's APEX-mini is 13.66 GiB and keeps the MTP head in the same file. Stock llama.cpp (b11151), no patch. Speed = 400 tokens ÷ full request time, temp 0, median of 3. Thinking was on (default), so the tokens are reasoning, not final prose or code. MTP drafts help one request, not 4 at once (same prompt): Solo prose: 129.6 → 152.9 tok/s Solo code: 127.2 → 190.9 tok/s 4x prose: 277.6 → 235.9 tok/s total 4x code: 307.9 → 277.5 tok/s total Peak VRAM: 13.0 GiB, 14.4 GiB with MTP. GSM8K, 100 problems, thinking off: 92/100, none cut off. Thinking on: 91/100, 2 still thinking at 129k tokens. Limits: GSM8K is my only quality check, no full-precision run (Myric: +11.7% perplexity vs BF16). Speed and VRAM at 2k context per request. MTP and 4 at once change the exact output.

Nemotron 3 Ultra performed GPT 5.5 level 10× cheaper We gave three same prompts to build HTML5 canvas with real physics. At first scene we have water in a spinning drum. Galton board - balls through pegs into bins. And a block collision setup with extreme mass differences. Outputs: Nemotron 3 Ultra: 11.3k tokens, $0.051 GPT 5.5: 11.0k tokens, $0.57 Nemotron stays right on GPT 5.5's heels, but at 10× cheaper. The gap in quality is far smaller than the gap in price.

nemotron 3 nano omni-30B reasoning at Q8 running autonomously on my dgx spark right now. 58 tok/s. 1 million context. multimodal. hermes agent is using it to research xAI's new grok algorithm that dropped yesterday. pulling repos. scanning code. breaking it down. all while i post this. 30B model. 58 tokens per second. 1M context. reads images and video. locally. for free. nobody is talking about this model and that's insane.