NVIDIA releases Nemotron 3.5 Bolt E4B, a 4GB open model for edge agents

NVIDIA has released Nemotron 3.5 Bolt E4B 4GB on Hugging Face and Ollama, an open-weight small language model built on Google’s Gemma 4 E4B base and refined with Nemotron post-training. Designed for on-device voice assistants, interactive game non-player characters and robotics, the model supports a 64k-token context window, 35 languages, multi-turn tool calling, a speech-ready conversational output mode that strips markdown formatting for text-to-speech pipelines and configurable reasoning budgets.

Across conversational and agentic evaluations, Nemotron 3.5 Bolt E4B delivers roughly 40% higher average accuracy than NVIDIA’s earlier Nemotron 3 Nano 4B while generating up to 80% fewer output tokens than competing reasoning models. On Scale AI’s MultiChallenge multi-turn instruction benchmark, it ranks first among small language models at 56.03%, 12.83 percentage points ahead of the base Gemma 4 E4B model (43.2%).

When quantised to Q4_K_M on llama.cpp with a 3k-token prompt, the model occupies 3.5GB of VRAM on a GeForce RTX 5070 12GB laptop GPU at 133.4 tokens per second (548 ms time to first token), 3.1GB on RTX Spark 32GB at 67.0 tokens per second and 4.2GB on a Jetson Orin Nano 8GB module at 15.5 tokens per second.