NVIDIA-Nemotron-3.5-Bolt-E4B-4GB
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,...
Params
4.0 B
Context
—
Downloads 30d
132 K
Likes
394
Download history
tracking started — chart appears after 7 days of snapshots (1 recorded)132 K downloads in the last 30 days
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf | Q4_K_M | 6.2 GB | 7.9 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Run it
copy-paste, exact tags checked against the Hub$ ollama run nvidia-nemotron-3-5-bolt-e4b-4gb # pin the quantization explicitly $ ollama run nvidia-nemotron-3-5-bolt-e4b-4gb-q4_k_m
$ huggingface-cli download nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-GGUF \
Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf --local-dir .
$ llama-cli -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf \
-c 8192 -ngl 99 -t 8 --color
$ curl -s https://aimodelscomparison.com/api/v1/models/nvidia-nemotron-3-5-bolt-e4b-4gb
{
"hf_id": "nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB",
"params_b": 4.00,
"context_length": null,
"license": { "id": "other", "commercial": "unknown" },
"downloads_30d": 132176,
"vram_estimates": [
{ "quant": "Q4_K_M", "gb": 7.9 }
],
"updated_at": "2026-11-10T01:00:54Z"
}
Specifications
- Parameters
- 4.0 B
- Licence
- other
- First seen on the Hub
- 2026-10-05
- Training datasets
- nvidia/nemotron-post-training-v3, nvidia/nemotron-pre-training-datasets
- Added to our catalog
- 2026-11-10
Compare with any text-generation model