Model reference · open weights
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.
Summary of the nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB model card, 2026-10-25
What it is
| Released by | NVIDIA |
|---|---|
| Released | 2026-10-05 |
| VRAM | 6.2 GB for the weights |
What it runs on
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.
| Card | Weights alone |
|---|---|
| RTX 3060 12 GB | fits |
| RTX 4060 Ti 16 GB | fits |
| RTX 3090 24 GB | fits |
| RTX 4090 24 GB | fits |
| RTX 5090 32 GB | fits |
| L40S 48 GB | fits |
| A100 80 GB | fits |
| H100 80 GB | fits |
| RTX PRO 6000 Blackwell 96 GB | fits |
| DGX Spark (GB10) 128 GB unified | fits |
| H200 141 GB | fits |
| B200 180 GB | fits |
From the model card
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.
| Total Parameters | 8B (4B effective) |
| Architecture | Gemma-4-E4B |
| Precision | Q4_K_M |
| Context Length | Input: up to 64K tokens; output: up to 64K tokens |
| Inputs / Outputs | Text / text |
| Deployment | llama.cpp (recipe pending) |
| Reasoning Mode | Chat-template controls (enable_thinking=True/False) and runtime thinking budget |
| Conversational Mode | Chat-template controls (conversational_mode=True/False) |
| Recommended Sampling | Temperature 1.0, top_p 0.95 in both thinking modes |
| Best For | Local voice and device assistants, interactive personas, and embedded interfaces |
| License | OpenMDW-1.1 |
Model Developer: NVIDIA Corporation
Model Dates: December 2025 - May 2026
Data Freshness:
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.
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 "$MODEL_DIR"
docker run --rm --gpus all \
-p 8000:8000 \
-v "$MODEL_DIR:/models:ro" \
ghcr.io/ggml-org/llama.cpp:server-cuda13-b11459 \
-m "/models/$MODEL_FILE" \
--alias nemotron-bolt \
--host 0.0.0.0 \
--port 8000 \
-c 65536 \
-ngl 999 \
--jinja
docker run --rm --gpus all \
-p 8000:8000 \
-v "$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!
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
We evaluated our model on the following benchmarks:
| Benchmark | Nemotron-3.5Bolt-E4BBF16 | Gemma4E4B-itBF16 | Qwen3.54BBF16 | Nemotron-3Nano-4BBF16 | Nemotron-3.5Bolt-E4B-4GBQ4_K_M | Gemma4E4B-itQ4_K_M | Qwen3.54BQ4_K_M | Nemotron-3Nano-4BQ4_K_M |
|---|---|---|---|---|---|---|---|---|
| MultiChallenge | 53.10 | 42.08 | 51.76 | 35.30 | 55.60 | 39.36 | 52.23 | 34.53 |
| RoleBench | 67.05 | 49.95 | 53.66 | 12.02 | 71.21 | 60.51 | 52.35 | 10.86 |
| RoleMRC | 80.63 | 74.62 | 65.81 | 47.00 | 79.12 | 75.24 | 62.61 | 45.84 |
| EQ-Bench v3 | 39.40 | 41.17 | 32.92 | 19.31 | 38.15 | 39.77 | 32.07 | 19.45 |
| Brevity Benchmark | 82.52 | 79.99 | 75.35 | 73.60 | 82.84 | 78.49 | 75.17 | 70.98 |
| BFCL-v3 | 68.51 | 66.39 | 70.95 | 61.10 | 67.31 | 65.03 | 70.56 | 59.07 |
| When2Call Multi-Turn | 63.36 | 62.90 | 37.85 | 27.70 | 60.50 | 61.12 | 42.88 | 35.96 |
| ToolSandbox | 68.77 | 65.43 | 69.54 | 57.88 | 74.13 | 68.31 | 74.51 | 59.78 |
| Tau2-Bench | 55.10 | 38.50 | 75.06 | 29.20 | 52.32 | 38.72 | 61.74 | 14.89 |
| IHEval | 84.79 | 67.04 | 60.57 | 51.31 | 82.40 | 66.61 | 62.87 | 53.75 |
| AgentIF | 25.81 | 21.89 | 20.44 | 20.08 | 22.95 | 20.05 | 22.24 | 21.25 |
| StructEval-T | 80.40 | 79.27 | 76.01 | 63.50 | 79.99 | 77. |
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
How it works