Nemotron 3.5 Bolt
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Headlines · 4
- NVIDIA and Hugging Face Demonstrate Microduck Sim-to-Real Training
NVIDIA and Hugging Face published a walkthrough for training the open-source Microduck bipedal robot with NVIDIA Isaac Lab and Newton, then transferring the learned locomotion policy to physical hardware. The workflow uses AI agents to plan a simulated obstacle course and prepare assets, while Isaac Sim evaluates trained policies on RTX Spark; the demonstrations show Microduck walking and recovering from a fall. Pollen Robotics says the robot stands 25 centimeters tall, weighs under 800 grams and has 15 motors, with preorders priced at $399 before taxes and shipping.
TechRepublic · 🔥 0 - NVIDIA expands open-model lineup across seven domains
NVIDIA announced a broad expansion of its open-model portfolio at GTC Berlin, adding Nemotron 3.5 Super VL and Bolt, Nemotron Retriever models, and Kumo open-weight foundation models for structured business data. The lineup also includes Cosmos 3.5 world models, Alpamayo 2 Edge for autonomous vehicles, BioNeMo RFD4-Proteina for protein design, and Ising for quantum computing, with distribution through platforms including Hugging Face, GitHub, build.nvidia.com and NVIDIA NIM. NVIDIA says Nemotron models support multimodal agents, orchestration and reasoning, while RFD4-Proteina was released with the UW Medicine Institute for Protein Design.
NVIDIA Blog · 🔥 0 - Hands-on: Nemotron 3.5 Bolt suits small multiuser setups with long context
A Reddit user posting in r/LocalLLaMA as /u/1ncehost says Nemotron 3.5 Bolt was the right size for their 128 GB setup (4x MI100) and works for small multiuser workloads. The user reports that Bolt's decoders are 2-bit quantized with other sensitive parts in NVFP4 in the stock model, and that it pairs one full-attention (GQA) layer with every five linear-attention (Mamba2) layers, giving a narrower attention footprint than Qwen3: about 200 MB at 30k context and 1 GB at 256k, versus roughly 3.4 GB for Flash Next and 8.5 GB for 27B. The poster describes Bolt as essentially a REAP-ed Nemotron 3.5 Super with additional post-training, rates its intelligence as roughly on par with Qwen3.6 27B, says it runs about twice as fast as 27B on their hardware, and singles out its non-hallucination rate and memory-efficient long context and prefix cache.
r/LocalLLaMA · 🔥 0 - Nvidia releases 120B Nemotron 3.5 Super VL and two Bolt edge models
Nvidia released Nemotron 3.5 Super VL, an open-weight multimodal mixture-of-experts model with 120 billion total and 12 billion active parameters, aimed at always-on agents and combining reasoning, instruction following and tool use with text, image, document and video understanding. Weights are available on Hugging Face in BF16 and NVFP4, organizations can post-train it with Nvidia NeMo on their own data and workflows, and both vLLM and SGLang announced day-0 serving support. Nvidia also introduced the Nemotron 3.5 Bolt line: the 74B-A9B 24GB, a 74-billion-parameter, 9-billion-active model compressed with 2-bit quantization to fit 24GB of memory, is available now, while a roughly 4GB Bolt E4B is planned for October 23. Nvidia claims up to 5x the throughput of comparable open models on agentic tasks, an 80.8% CharXiv chart-reasoning score (84% with Python code execution), and that Dynamo can serve up to 8x as many concurrent users with 85% lower time to first token under specified conditions; these figures are vendor-reported and not independently verified.
vLLM Blog · 🔥 0
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