NVIDIA Nemotron 3.5 Bolt: small open model for robots in ~4GB
Why it matters
NVIDIA's Nemotron 3.5 Bolt E4B is a small open language model built for local assistants, interactive characters and robots that carry on a conversation and use tools, designed to fit in as little as ~4GB. It builds on Google's Gemma 4 E4B with NVIDIA's Nemotron post-training: 8B parameters (4B effective) and up to 64K tokens of context. In NVIDIA's demo on a Jetson Orin Nano 8GB, Bolt calls Reachy Mini's animation scripts and powers its spoken responses while Cosmos 3 Edge handles vision. On MultiChallenge (consistency across turns) it scores 53.1, versus 42.1 for Gemma 4 E4B and 35.3 for Nemotron 3 Nano 4B; NVIDIA reports roughly 40% higher average scores than Nano 4B, about 10% above other peers tested, and up to ~80% fewer output tokens. On an RTX 5070 it uses 3.5GB of VRAM, with 548 ms to first token and 133.4 tokens/s (Q4_K_M, llama.cpp). Caveats: these are NVIDIA's own measurements, and Qwen3.5 4B still leads on some tool-use tests (Tau2-Bench 75.1 vs 55.1). Weights, data and recipes are open on Hugging Face.
Read the source: NVIDIA (research blog) (opens in a new tab)
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