NVIDIA opens the 120B Nemotron 3.5 Super VL with datasets and training recipes; the 74B-A9B Bolt fits in 24GB with native 2-bit quantization
NVIDIA rounds out Nemotron 3.5 into a set of open agent models spanning data centers to laptops.
// Key points
- Nemotron 3.5 Super VL is an open 120B-parameter model that NVIDIA says matches its largest reasoning model at about a fifth of the parameters and runs 5x faster than the next best in its class.
- Super VL was trained on about 26 trillion tokens of pretraining and continued pretraining, then about 3.8 million post-training samples plus RL; weights, datasets and recipes are open, and it is on Hugging Face, OpenRouter, ModelScope and NVIDIA NIM.
- Nemotron 3.5 Bolt 74B-A9B fits in 24GB using native 2-bit quantization and is on Hugging Face with support for llama.cpp, Ollama, LM Studio, Unsloth and vLLM.
- A smaller Bolt E4B 4GB arrives October 23; the article does not name a license.
Builder's takeIn the 10-14 issue this was still a preview; today Bolt is actually on Hugging Face, and a 74B agent model runs in 24GB. I’ll test Bolt locally on PandaClaws’ batch rewriting to see how much 2-bit quantization costs on long Chinese text; Super VL, with its datasets and recipes, suits teams that want to fine-tune. Until the license is clear, evaluate only and keep it out of production.
// Background · from #Open models
Full timeline →- Oct 16 Meta releases open weights for Muse Spark Open, an 816B-total, 42B-active multimodal MoE with a 1M context
- Oct 16 Mistral releases Timestral time-series models from 5M to 209M parameters, forecasting a day ahead in 140 ms on a CPU
- Oct 15 Microsoft releases UserRepGen-4B under MIT: it turns multi-turn conversations into user descriptions for simulators, scoring 4.95 on fidelity against GPT-5.5’s 3.90