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NVIDIA Nemotron 3.5 Bolt E4B 4GB: A Local Small Model

An open small language model released 23 October 2026, aiming to run conversational agents at roughly a 4GB footprint — NVIDIA claims about 40% higher average quality than its predecessor and up to 80% fewer output tokens than peers

Official Source

Summarised from NVIDIA's technical report — benchmark numbers are vendor-measured.

Features

  • ✓ Built on Gemma 4 E4B with Nemotron post-training; NVIDIA reports roughly 40% higher average quality than Nemotron 3 Nano 4B on key conversational agent benchmarks
  • ✓ Designed to fit in about 4GB for its target workloads, leaving headroom for adjacent speech and vision models
  • ✓ 64k context, support for 35 languages on a base model pre-trained on 140+ languages
  • ✓ Three useful switches: multi-turn tool use, a conversational mode that keeps replies short and speech-ready, and configurable reasoning
  • ✓ Runs on GeForce RTX, Jetson, RTX Spark and DGX Spark; the official demo drives a Reachy Mini robot on a Jetson Orin Nano 8GB

How to Use

  1. Pull nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB from Hugging Face and confirm your hardware is RTX, Jetson or Spark class
  2. Pick a mode for the job: conversational mode for device assistants and characters, multi-turn tool use for workflow tasks
  3. Cap the reasoning budget — for realtime voice you must limit thinking length or time-to-first-token ruins the conversation feel
  4. Re-test on your own evaluation set: vendor benchmarks are a starting point, your application data is the acceptance criteria

FAQ

What is Nemotron 3.5 Bolt E4B 4GB?

An open small language model NVIDIA released on 23 October 2026 (technical report: https://research.nvidia.com/labs/nemotron/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB). It is optimised for intelligence and speed per GB in conversational agents, aimed at voice and device assistants, interactive characters, and robotics or IoT interfaces.

What does the 4GB figure actually mean?

NVIDIA's wording is that it is designed to pack conversational agent intelligence into as little as ~4GB in its target workloads, explicitly preserving headroom for applications and other models in the same pipeline. That is the model's own footprint in a target workload, not the requirement that the whole device live in 4GB of memory — the point is leaving room for speech recognition or vision models beside it.

How does it compare with its predecessor and peer small models?

The technical report lists Brevity Bench 82.5 (Nemotron 3 Nano 4B 73.6, Qwen 3.5 4B 75.3, Gemma 4 E4B 80.0), MultiChallenge 53.1 (versus 35.3, 51.8 and 42.1), RoleBench 67.0 (versus 12.0, 53.7, 49.9) and RoleMRC 80.6. NVIDIA also claims up to roughly 80% fewer output tokens than peers. All of it is vendor-measured, so verify against your own dialogue data.

How much context and language coverage?

The report states a 64k context, used to hold conversation history, instructions, tool definitions, retrieved information and application state, plus support for 35 languages on a base model pre-trained on more than 140. For a local device assistant 64k is usually enough, though longer context pushes time-to-first-token up.

Where does it fit, and where does it not?

NVIDIA names three families: device and voice assistants, virtual and interactive characters such as game NPCs and companions, and embedded or industrial agents spanning robots, heavy machinery interfaces and industrial IoT. It is positioned as a fast conversational model, so long deep-reasoning work is not the target; the report's own comparison chart places cloud flagships such as Gemini 3.8 Flash and Claude Sonnet 5.5 as the reference points above it.