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// NVIDIA Nemotron · Text LLM

Llama-3.1-Nemotron-Nano-4B-v1.1: VRAM & local deployment

4.51B parameters. About 4.6 GB at 4-bit and 11.5 GB at BF16 with one 8K-token request.

released 2025-05-03licence: Commercial with conditionsmemory figures are estimates

01Specs

PublisherNVIDIA · nvidia
Family / seriesNVIDIA Nemotron · Llama-3.1-Nemotron-Nano
TypeText LLM (text-generation)
Parameters4.51B Source: counted from official safetensors weights
Context length128K tokens (131,072)max_position_embeddings in config.json
Architecture32 layers · GQA
Released weightsBF16 · 9.0 GBdownload
Licencenvidia-open-model-license · Commercial with conditionsCommercial use allowed under the publisher’s own licence terms (user caps, acceptable-use policy, attribution or region limits). Read it before shipping.
Released2025-05-03 (Hugging Face repo created)

02How much VRAM it needs (estimate)

Total = weights + 10% + 0.5GB overhead + KV cache for one request (FP16 KV). More concurrent users multiply the KV column.

PrecisionWeightsTotal @ 8KTotal @ 32KTotal @ 128KSmallest GPUMac
BF16 / FP16 (original)9.0 GB 11.5 GB14.7 GB27.6 GB 16GB GPUMac 24GB
FP8 / INT8 / Q8_04.8 GB 6.9 GB10.1 GB23.0 GB 8GB GPUMac 16GB
4-bit (Q4_K_M / AWQ / GPTQ-Int4)2.7 GB 4.6 GB7.8 GB20.7 GB 8GB GPUMac 16GB

KV cache per request: 8K 1.1 GB · 32K 4.3 GB · 128K 17.2 GB

Bits per weight: BF16 16, FP8 8.5, 4-bit 4.85. “Fits” = total ≤ 90% of memory; Mac GPU can use ~2/3 of unified memory (≤36GB) or ~3/4 (larger). For MoE, all experts must be in memory, so size by total parameters. Method →

03How to run it

Only runtimes the publisher or the runtime’s official library names for this model.

Which inference engine to choose →

04Official repositories & quantised builds

1 official repo(s) from nvidia.

05Other NVIDIA Nemotron models

All 62 →
ModelParams4-bit totalReleased
Llama-3.1-Nemotron-Nano-8B-v1 · same series8.03B6.9 GB2025-03-16
NVIDIA-Nemotron-Labs-Dory1.0-4B-BF163.98B3.6 GB2026-10-16
Nemotron-3.5-Rerank-8B-BF168.38B6.1 GB2026-10-15
NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16124.5B / 12B83.5 GB2026-10-12
NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF1674.9B / 9B50.4 GB2026-10-09
Nemotron-3.5-Embed-1B-BF161.55B1.5 GB2026-10-07
Nemotron-3.5-Embed-8B-BF168.38B6.1 GB2026-10-07
Nemotron-3.5-Parse631M1.1 GB2026-09-03
Nemotron-3-Labs-Ultra-Math-RL550B / 55B367 GB2026-09-02

06Sources

Data checked 2025-10-15. Parameters, context and licence are copied from the official repository; memory figures are our estimates.

Data updated: Self-host open models(UTC+8)