NVIDIA Releases Nemotron 3.5 Super VL and Bolt 24GB, Both Available on Ollama

1. 06:03
   NVIDIA AI · X

   Big shoutout to all of our partners for making Nemotron 3.5 Super VL their own through post-training 🙌 Here’s what they’ve built 🧵

   Big shoutout to all of our partners for making Nemotron 3.5 Super VL their own through post-training 🙌 Here’s what they’ve built 🧵

1. 07:53
   Ollama · XPicks

   NVIDIA Nemotron 3.5 Bolt 74B-A9B 24GB is now on Ollama

   NVIDIA Nemotron 3.5 Bolt 74B-A9B 24GB is now on Ollama. This vision-language model (VLM) is designed to run everyday agentic tasks locally, using native 2-bit quantization to fit in 24GB of memory and run alongside other applications. ollama run nemotron-3.5-bolt

2. 07:50
   Ollama · X

   NVIDIA Nemotron 3.5 Super VL is now available on Ollama

   NVIDIA Nemotron 3.5 Super VL is now available on Ollama. The model combines reasoning and visual understanding across a range of agent workflows. Ollama says it delivers up to 5x higher throughput on a single GPU for agentic tasks compared with other open models of its size.

3. 04:04
   NVIDIA AI · X

   NVIDIA introduces Nemotron 3.5 Bolt 24GB for local agents

   NVIDIA AI introduces Nemotron 3.5 Bolt 24GB, saying it fits into 24GB of memory. The quoted @NVIDIARTXSpark post describes 74B-A9B parameters packed into 24GB of memory, optimized for RTX Spark and designed to run alongside your apps. https://t.co/SOiBgeMMjD https://t.co/yPsBt9N58A

4. 00:09
   The DecoderPicks

   Nvidia releases Nemotron 3.5 Super VL for continuously running enterprise AI agents

   Nvidia released Nemotron 3.5 Super VL, an open multimodal model with 120 billion parameters and 12 billion active per token, for continuously running enterprise agents. Nvidia says it approaches Nemotron 3 Ultra’s accuracy on specified agent tasks with about one-fifth the parameters, and is releasing weights, datasets, and training recipes. The accompanying Nemotron 3.5 Bolt 74B-A9B 24GB targets local agents, while Bolt E4B 4GB is planned for later this month.

1. 23:54
   vLLM · X

   RedHat_AI releases optimized NVIDIA Nemotron 3.5 Super VL checkpoints

   vLLM highlights @RedHat_AI’s FP8 and DSpark checkpoints for NVIDIA Nemotron 3.5 Super VL, a 120B multimodal MoE, using LLM Compressor and Speculators. RedHat_AI reports close to 2x performance on Hopper with accuracy intact. Its 4× H100 benchmarks show FP8 improving throughput under load and DSpark boosting per-user streaming speed, with both together outperforming either alone across HumanEval, math reasoning, and RAG workloads. Both checkpoints are open: https://huggingface.co/RedHatAI/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-FP8 https://huggingface.co/RedHatAI/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B.dspark

2. 23:50
   vLLM · X

   vLLM adds day-0 support for NVIDIA Nemotron 3.5 Super VL

   🎉 Day-0 support for NVIDIA Nemotron 3.5 Super VL in vLLM! 120B total parameters, 12B active per token, and a 1M-token context window. Text, images, documents and video in one open multimodal model, with reasoning and tool use for agent workflows. Here is what is supported and how to get started. 🧵 1/4

3. 23:09
   NVIDIA AI · XPicks

   NVIDIA introduces Nemotron 3.5 Super VL, an open model for long-running AI agents

   Meet NVIDIA Nemotron 3.5 Super VL. NVIDIA’s new open model combines reasoning and tool use with text, image, document, and video understanding for long-running AI agents. 120B total parameters with 12B active.

4. 23:00
   NVIDIA BlogPicks

   NVIDIA Expands Nemotron Family With Super VL and Bolt Open Models

   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.

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