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Can the NVIDIA GeForce RTX 4070 SUPER run Nemotron 3 Nano 30B-A3B?

64 ptsGood (B) · 64 of 100 points: Model quality 24.3/45, Memory fit 10.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, with part of the model in system RAM

Runs with offload. At Q2_K it needs about 13.9 GB of 12.0 GB usable, with an estimated speed of 73.4 tok/s.

Best quant
Q2_K
Generation speed
73.4 tok/s
Prompt processing
6,541 tok/s
First token (1K prompt)
0.3 s
Largest safe context
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Download Nemotron 3 Nano 30B-A3B

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run nemotron-3-nano:30b

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/ggml-org/NVIDIA-Nemotron-3-Nano-30B-A3B-GGUF (opens in a new tab)

ollama run hf.co/ggml-org/NVIDIA-Nemotron-3-Nano-30B-A3B-GGUF

Original weights · Hugging Face

Full-precision files from NVIDIA, for fine-tuning, vLLM, Transformers.

huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from NVIDIA. The weights themselves download from Hugging Face.

github.com/NVIDIA-NeMo/Nemotron (opens in a new tab)

git clone https://github.com/NVIDIA-NeMo/Nemotron

NVIDIA’s Nemotron repo with recipes for the whole family.

How the B grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.24.3 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.10.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Total64.3 / 10064 ptsGood (B) · 64 of 100 points: Model quality 24.3/45, Memory fit 10.0/25, Speed 15.0/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the NVIDIA GeForce RTX 4070 SUPER

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 13.9 GB 12.0 GB 73.4 tok/s Runs with offload 64 ptsGood (B) · 64 of 100 points: Model quality 24.3/45, Memory fit 10.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q3_K_M 16.7 GB 12.0 GB 38.1 tok/s Heavy offload 62 ptsGood (B) · 62 of 100 points: Model quality 29.1/45, Memory fit 4.0/25, Speed 14.6/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q4_K_M 20.5 GB 12.0 GB 22.7 tok/s Heavy offload 63 ptsGood (B) · 63 of 100 points: Model quality 32.9/45, Memory fit 4.0/25, Speed 11.3/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 23.7 GB 12.0 GB 16.5 tok/s Heavy offload 62 ptsGood (B) · 62 of 100 points: Model quality 33.6/45, Memory fit 4.0/25, Speed 9.6/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q6_K 27.1 GB 12.0 GB 12.7 tok/s Heavy offload 61 ptsGood (B) · 61 of 100 points: Model quality 34.3/45, Memory fit 4.0/25, Speed 8.4/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q8_0 34.7 GB 12.0 GB — Won't run 10 ptsWon't run (F) · 10 of 100 points: Model quality 10.4/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF (opens in a new tab)
F16 64.4 GB 12.0 GB — Won't run 10 ptsWon't run (F) · 10 of 100 points: Model quality 10.4/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.