Model reference · open weights

NVIDIA-Nemotron-3.5-Bolt-4GB

LLMs nvidia Text gen 1 build Its own licence terms 5k dl/mo

NVIDIA-Nemotron-3.5-Bolt-4GB is an open-weight language model from NVIDIA. NVIDIA-Nemotron-3.5-Bolt-E4B-4GB (BF16) weighs 6.2 GB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • NVIDIA-Nemotron-3.5-Bolt-4GB is a small language model with 8B total parameters and 4B effective parameters, designed for fast local voice assistants and embedded interfaces.
  • It supports a context length of up to 64K tokens and is licensed under the OpenMDW-1.1 agreement.
  • The model is post-trained from Gemma-4-E4B-it and supports languages including English, Spanish, French, German, Italian, and Japanese.

Summary of the nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB model card, 2026-10-25

What it is

Released byNVIDIA
Released2026-10-05
VRAM6.2 GB for the weights

What it runs on

Memory and cards for NVIDIA-Nemotron-3.5-Bolt-E4B-4GB (BF16)

6.2 GBweights, file size
651 MBruntime overhead, at least

How much memory each request adds isn't estimated yet for this architecture. The weights need at least the cards below, plus room for the context.

CardWeights alone
RTX 3060 12 GBfits
RTX 4060 Ti 16 GBfits
RTX 3090 24 GBfits
RTX 4090 24 GBfits
RTX 5090 32 GBfits
L40S 48 GBfits
A100 80 GBfits
H100 80 GBfits
RTX PRO 6000 Blackwell 96 GBfits
DGX Spark (GB10) 128 GB unifiedfits
H200 141 GBfits
B200 180 GBfits

From the model card

What NVIDIA says about NVIDIA-Nemotron-3.5-Bolt-4GB

Read the model card

Description

NVIDIA-Nemotron-3.5-Bolt-E4B-4GB is a small language model (SLM) further post-trained from Gemma-4-E4B-it by NVIDIA. Nemotron-3.5-Bolt-E4B carries forward Gemma’s strong conversational capabilities and further improves multi-turn tool use, instruction following, and input grounding for fast local voice/device assistants, interactive personas, and embedded/edge interfaces. Designed for low-memory, low-TTFT operation, the model allows agents to respond quickly while preserving headroom for applications or adjacent model pipelines.

For speculative decoding, use the companion DFlash drafter. For the full-precision (BF16) version, see NVIDIA-Nemotron-3.5-Bolt-E4B-BF16.

This model is ready for commercial or non‑commercial use.

Model Summary

Total Parameters8B (4B effective)
ArchitectureGemma-4-E4B
PrecisionQ4_K_M
Context LengthInput: up to 64K tokens; output: up to 64K tokens
Inputs / OutputsText / text
Deploymentllama.cpp (recipe pending)
Reasoning ModeChat-template controls (enable_thinking=True/False) and runtime thinking budget
Conversational ModeChat-template controls (conversational_mode=True/False)
Recommended SamplingTemperature 1.0, top_p 0.95 in both thinking modes
Best ForLocal voice and device assistants, interactive personas, and embedded interfaces
LicenseOpenMDW-1.1

Model Overview

Model Developer: NVIDIA Corporation

Model Dates: December 2025 - May 2026

Data Freshness:

  • The post-training data has a cutoff date of May 2026.

What is Nemotron?

NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.

Quick Start

export MODEL_REPO=nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB
export MODEL_FILE=Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf

export DRAFTER_REPO=nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-DFlash
export DRAFTER_FILE=Nemotron-3.5-Bolt-E4B-DFlash-BF16.gguf

export MODEL_DIR="$PWD/bolt-models"

hf download "$MODEL_REPO" "$MODEL_FILE" \
  --local-dir "$MODEL_DIR"

hf download "$DRAFTER_REPO" "$DRAFTER_FILE" \
  --local-dir "$MODEL_DIR"

Without drafter

docker run --rm --gpus all \
  -p 8000:8000 \
  -v "$MODEL_DIR:/models:ro" \
  ghcr.io/ggml-org/llama.cpp:server-cuda13-b11459 \
  -m "/models/$MODEL_FILE" \
  --alias nemotron-bolt \
  --host 0.0.0.0 \
  --port 8000 \
  -c 65536 \
  -ngl 999 \
  --jinja

With DFlash drafter

docker run --rm --gpus all \
  -p 8000:8000 \
  -v "$MODEL_DIR:/models:ro" \
  ghcr.io/ggml-org/llama.cpp:server-cuda13-b11459 \
  -m "/models/$MODEL_FILE" \
  --spec-draft-model "/models/$DRAFTER_FILE" \
  --spec-type draft-dflash \
  --spec-draft-n-max 7 \
  --spec-draft-ngl 999 \
  --alias nemotron-bolt \
  --host 0.0.0.0 \
  --port 8000 \
  -c 65536 \
  -ngl 999 \
  --jinja
curl --fail-with-body -sS \
  http://localhost:8000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "nemotron-bolt",
    "messages": [
      {"role": "user", "content": "What is 17 multiplied by 23?"}
    ],
    "temperature": 0.0,
    "max_tokens": 512,
    "chat_template_kwargs": {
      "enable_thinking": true
    }
  }'

For more details on how to deploy and use the model — see the Quick Start Guide below!

License/Terms of Use

Governing Terms: Use of this model is governed by the OpenMDW License Agreement, version 1.1. This model has been post-trained on the Google Gemma 4 model, and use of the Google Gemma 4 model is governed by Apache License, Version 2.0

Benchmarks

Reasoning Benchmark Evaluations

Benchmarks

We evaluated our model on the following benchmarks:

BenchmarkNemotron-3.5Bolt-E4BBF16Gemma4E4B-itBF16Qwen3.54BBF16Nemotron-3Nano-4BBF16Nemotron-3.5Bolt-E4B-4GBQ4_K_MGemma4E4B-itQ4_K_MQwen3.54BQ4_K_MNemotron-3Nano-4BQ4_K_M
MultiChallenge53.1042.0851.7635.3055.6039.3652.2334.53
RoleBench67.0549.9553.6612.0271.2160.5152.3510.86
RoleMRC80.6374.6265.8147.0079.1275.2462.6145.84
EQ-Bench v339.4041.1732.9219.3138.1539.7732.0719.45
Brevity Benchmark82.5279.9975.3573.6082.8478.4975.1770.98
BFCL-v368.5166.3970.9561.1067.3165.0370.5659.07
When2Call Multi-Turn63.3662.9037.8527.7060.5061.1242.8835.96
ToolSandbox68.7765.4369.5457.8874.1368.3174.5159.78
Tau2-Bench55.1038.5075.0629.2052.3238.7261.7414.89
IHEval84.7967.0460.5751.3182.4066.6162.8753.75
AgentIF25.8121.8920.4420.0822.9520.0522.2421.25
StructEval-T80.4079.2776.0163.5079.9977.

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

How it works

How language models work

Your prompttext / messagesTransformerattention over tokensNext-token loopgenerate + streamResponsetext · tool callsA language model reads your tokens and predicts the next one, again and again, streaming the reply back.
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