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Can the NVIDIA GB10 (128 GB) run Kimi Linear 48B-A3B?

82 ptsGreat (A) · 82 of 100 points: Model quality 33.0/45, Memory fit 25.0/25, Speed 14.8/15, Memory use 10.0/15

Yes, it runs well

Runs well. At Q6_K it needs about 50.2 GB of 115 GB usable, with an estimated speed of 38.8 tok/s.

Best quant
Q6_K
Generation speed
38.8 tok/s
Prompt processing
7,440 tok/s
First token (1K prompt)
0.3 s
Largest safe context
1,024K

Download Kimi Linear 48B-A3B

Recommended for the NVIDIA GB10 (128 GB): Q6_K. Every other quantization has its own download link in the table below.

Quantized files · GGUF

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

Download the Q6_K file (opens in a new tab)

huggingface.co/bartowski/moonshotai_Kimi-Linear-48B-A3B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/bartowski/moonshotai_Kimi-Linear-48B-A3B-Instruct-GGUF:Q6_K

Original weights · Hugging Face

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

huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Instruct (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/MoonshotAI/Kimi-Linear (opens in a new tab)

git clone https://github.com/MoonshotAI/Kimi-Linear

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How the A 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.33.0 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.14.8 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.10.0 / 15
Total82.8 / 10082 ptsGreat (A) · 82 of 100 points: Model quality 33.0/45, Memory fit 25.0/25, Speed 14.8/15, Memory use 10.0/15

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

Every quantization on the NVIDIA GB10 (128 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 29.5 GB 115 GB 91.7 tok/s Runs well 69 ptsGood (B) · 69 of 100 points: Model quality 23.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.9/15 GGUF (opens in a new tab)
Q3_K_M 34.0 GB 115 GB 72.2 tok/s Runs well 74 ptsGreat (A) · 74 of 100 points: Model quality 28.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.8/15 GGUF (opens in a new tab)
Q4_K_M 39.8 GB 115 GB 55.9 tok/s Runs well 79 ptsGreat (A) · 79 of 100 points: Model quality 31.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.0/15 GGUF (opens in a new tab)
Q5_K_M 44.8 GB 115 GB 46.1 tok/s Runs well 81 ptsGreat (A) · 81 of 100 points: Model quality 32.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.0/15 GGUF (opens in a new tab)
Q6_K 50.2 GB 115 GB 38.8 tok/s Runs well 82 ptsGreat (A) · 82 of 100 points: Model quality 33.0/45, Memory fit 25.0/25, Speed 14.8/15, Memory use 10.0/15 GGUF (opens in a new tab)
Q8_0 61.9 GB 115 GB 28.4 tok/s Runs well 83 ptsGreat (A) · 83 of 100 points: Model quality 33.3/45, Memory fit 25.0/25, Speed 12.6/15, Memory use 12.4/15 GGUF · folder (opens in a new tab)
F16 108 GB 115 GB 13.1 tok/s Tight fit 80 ptsGreat (A) · 80 of 100 points: Model quality 33.3/45, Memory fit 24.0/25, Speed 8.6/15, Memory use 15.0/15 GGUF · folder (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.