NVIDIA Expands Nemotron Family With Super VL and Bolt Open Models
Open From Data Center to Desktop: NVIDIA Expands Nemotron Family With Highest Efficiency Super VL and Bolt Models
- Who
- NVIDIA
- What
- released Nemotron 3.5 Super VL and two Nemotron 3.5 Bolt models
- When
- Quote
NVIDIA today released Nemotron 3.5 Super VL, an open 120-billion-parameter model that delivers the fastest task completion and highest accuracy for specialized always-on agents.
What happened
NVIDIA released Nemotron 3.5 Super VL, an open 120-billion-parameter model for multimodal agents, and Nemotron 3.5 Bolt 74B-A9B 24GB for local agentic workloads; Bolt E4B 4GB is coming Oct. 23. Super VL targets tool use, business workflows and visual document understanding, with open weights, datasets and training recipes. Bolt 74B-A9B uses native 2-bit quantization to fit in 24GB of memory, while Bolt E4B targets physical and conversational agents.
Why it matters
Open weights, datasets and training recipes give organizations a path to adapting agents to their own workflows while meeting local deployment and data residency requirements.
Reported by 2 sources
The story so far
NVIDIA released Nemotron 3.5 Super VL and Nemotron 3.5 Bolt 74B-A9B 24GB, and both are now available on Ollama. A smaller Bolt E4B 4GB model is scheduled for Oct. 23. Super VL is an open multimodal model with 120 billion total parameters and 12 billion active parameters. It combines reasoning and tool use with text, image, document and video understanding for agent workflows. NVIDIA released its weights, datasets and training recipes. vLLM added launch-day support and lists a 1-million-token context window; Red Hat AI also released open FP8 and DSpark optimized checkpoints. Bolt 74B-A9B targets local agent tasks and uses native 2-bit quantization to fit in 24GB of memory. NVIDIA says it is optimized for RTX Spark and designed to run alongside other applications. It is also available on Hugging Face, with support for llama.cpp, LM Studio, Unsloth and vLLM.
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