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Can the NVIDIA RTX PRO 6000 Blackwell run DeepSeek V2.5 1210?

62 ptsGood (B) · 62 of 100 points: Model quality 22.7/45, Memory fit 10.0/25, Speed 14.9/15, Memory use 15.0/15

Yes, with part of the model in system RAM

Runs with offload. At Q2_K it needs about 100 GB of 96.0 GB usable, with an estimated speed of 39.6 tok/s.

Best quant
Q2_K
Generation speed
39.6 tok/s
Prompt processing
4,150 tok/s
First token (1K prompt)
0.3 s
Largest safe context
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Download DeepSeek V2.5 1210

Recommended for the NVIDIA RTX PRO 6000 Blackwell: Q2_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 Q2_K files (opens in a new tab)

huggingface.co/bartowski/DeepSeek-V2.5-1210-GGUF (opens in a new tab)

ollama run hf.co/bartowski/DeepSeek-V2.5-1210-GGUF

Original weights · Hugging Face

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

huggingface.co/deepseek-ai/DeepSeek-V2.5-1210 (opens in a new tab)

No sign-in needed.

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

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.22.7 / 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.14.9 / 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
Total62.6 / 10062 ptsGood (B) · 62 of 100 points: Model quality 22.7/45, Memory fit 10.0/25, Speed 14.9/15, 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 RTX PRO 6000 Blackwell

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 100 GB 96.0 GB 39.6 tok/s Runs with offload 62 ptsGood (B) · 62 of 100 points: Model quality 22.7/45, Memory fit 10.0/25, Speed 14.9/15, Memory use 15.0/15 GGUF · folder (opens in a new tab)
Q3_K_M 121 GB 96.0 GB — Won't run 8 ptsWon't run (F) · 8 of 100 points: Model quality 8.2/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
Q4_K_M 150 GB 96.0 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 9.2/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
Q5_K_M 173 GB 96.0 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 9.4/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
Q6_K 199 GB 96.0 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 9.6/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
Q8_0 256 GB 96.0 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 9.7/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
F16 477 GB 96.0 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 9.7/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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.