NVIDIA released Nemotron 3.5 Bolt E4B 4GB on Friday, the small edge model its October 15 Nemotron launch had scheduled for October 23. It is post-trained from Google's Gemma 4 E4B using reinforcement learning alone, has 8 billion total parameters (4 billion effective), a 64K-token context window and a switchable thinking mode, and is open under the OpenMDW-1.1 license with commercial use allowed.

The model is built to run locally: NVIDIA targets local assistants, interactive characters and robots, and demoed a Reachy Mini robot chatting and moving on an 8GB Jetson Orin Nano, with Bolt handling conversation and Cosmos handling vision. It is available on Hugging Face, Ollama, LM Studio, Unsloth and Jetson AI Lab, and as an NVIDIA NIM. The '4GB' name is a memory-envelope target; the downloadable Q4_K_M file is 6.18 GB.

In NVIDIA's own BF16 measurements, Bolt scores 84.8 on the instruction-hierarchy benchmark IHEval against 67.0 for the Gemma model it started from, and beat Gemma 4 E4B, Qwen3.5-4B and Nemotron 3 Nano 4B on 15 of 21 evaluations in reasoning mode — though it trails Qwen3.5-4B badly on Tau2-Bench, 55.1 to 75.1. With its DFlash speculative-decoding drafter, NVIDIA reports up to 272.6 tokens per second on an RTX 5070.