# NVIDIA Nemotron 3.5: 120B Open Agent Model — 4GB Bolt Ships Oct 23 > Source: > Published: 2026-10-16 17:07:35+00:00 NVIDIA released three open Nemotron 3.5 models on October 15. Two are live now. The third — a 4GB edge model called Bolt E4B — arrives October 23. If you have been waiting for a local AI agent that fits in the same VRAM budget as a game, that is your date. ## Three Models, Three Use Cases The Nemotron 3.5 lineup covers data center to desktop edge: - **Nemotron 3.5 Super VL (120B / 12B active)** — Server and enterprise agents. Hybrid Mamba-Transformer mixture-of-experts architecture, 1M token context, accepts text, images, video, audio, and documents in a single pass. Available now on Hugging Face and NVIDIA Build. - **Nemotron 3.5 Bolt 74B-A9B (24GB)** — Workstation and laptop. Compressed to 24GB, runs on RTX Spark, DGX Spark, A100, and H100. GGUF and BF16 weights are already on Hugging Face. Available now. - **Nemotron 3.5 Bolt E4B (4GB)** — Edge. GeForce RTX, NVIDIA Jetson, DGX Spark. Built for voice assistants, interactive characters, and embedded agents with multi-turn tool use. Ships October 23. The 4GB framing is what makes this launch interesting. NVIDIA is targeting physical AI on Jetson — the kind of always-on agent that runs alongside a robotics stack — alongside consumer GPU deployments. The comparison class for E4B is Phi-4 Mini and Gemma 3 4B. It is not a frontier model; expect tradeoffs in reasoning depth. But multi-turn tool use at 4GB is genuinely new territory. ## Running Bolt 74B Right Now The 74B Bolt is available today and works with the usual local inference stack. The simplest paths: ``` # llama.cpp brew install llama.cpp llama serve -hf unsloth/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-GGUF:IQ2_XXS # Ollama ollama pull nvidia/nemotron-3.5-bolt-74b ollama run nvidia/nemotron-3.5-bolt-74b ``` vLLM works on Ampere, Hopper, and Blackwell. LM Studio and Unsloth both have support. The Unsloth GGUF quantization reports 84 tok/s on RTX Spark hardware — fast enough to serve as an interactive agent backend. ## What Super VL Is Actually Built For Super VL’s post-training story is where NVIDIA went beyond base capabilities. The model was fine-tuned on specialized enterprise tasks using GRPO reinforcement learning via NeMo RL, and NVIDIA reports 9–44% improvement over Claude Opus 5 and GPT-6.1 Sol on five domain benchmarks: document intelligence, security resilience, financial receipt extraction, scientific experimentation, and medical insurance claims. The practical capabilities: tool calling with `auto`, `required`, or `named` function selection; structured JSON output via schema; and a reasoning mode. Atlassian, CrowdStrike, Perplexity, Salesforce, and Synopsys are named early integration partners. NVIDIA’s developer blog has a [full walkthrough of multimodal agentic use cases](https://developer.nvidia.com/blog/nvidia-nemotron-3-5-super-vl-powers-specialized-agents-with-multimodal-reasoning/), including document extraction with schema-constrained JSON output. ## Read the License Before You Ship The marketing says “open” and the training infrastructure — NeMo RL and NeMo Gym — is Apache 2.0. The model weights are not. Both Super VL and Bolt are released under the **OpenMDW License Agreement v1.1**. Check whether your use case — commercial deployment, fine-tuning, redistribution — is covered before it becomes a conversation with your legal team after launch. The license is on [Hugging Face alongside the Bolt 74B weights](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16). Training recipes and usage cookbooks are in the [NVIDIA-NeMo/Nemotron GitHub repository](https://github.com/NVIDIA-NeMo/Nemotron). ## Mark October 23 Super VL is the model enterprises will evaluate. Bolt 74B is the model developers will run locally this week. Bolt E4B is the model to watch — it is the first serious attempt at a fully agentic model in a 4GB footprint, and the Jetson target signals NVIDIA’s intent to own the physical AI edge stack from model to hardware. A week from now you will be able to test that claim on any modern GeForce GPU. Details are in [VentureBeat’s benchmark breakdown](https://venturebeat.com/technology/nvidias-new-open-nemotron-3-5-super-vl-with-post-training-beats-claude-opus-5-gpt-6-1-sol-on-specialized-task-benchmarks) and on the [official NVIDIA Nemotron 3.5 blog post](https://blogs.nvidia.com/blog/nemotron-3-5-super-vl/).