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Can the NVIDIA RTX PRO 6000 Blackwell run Solar Open 100B?

89 ptsExcellent (S) · 89 of 100 points: Model quality 34.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q5_K_M it needs about 79.9 GB of 96.0 GB usable, with an estimated speed of 75.7 tok/s.

Best quant
Q5_K_M
Generation speed
75.7 tok/s
Prompt processing
7,500 tok/s
First token (1K prompt)
0.3 s
Largest safe context
88K

Download Solar Open 100B

Recommended for the NVIDIA RTX PRO 6000 Blackwell: Q5_K_M. 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 Q5_K_M file (opens in a new tab)

huggingface.co/mradermacher/Solar-Open-100B-i1-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/Solar-Open-100B-i1-GGUF:Q5_K_M

Original weights · Hugging Face

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

huggingface.co/upstage/Solar-Open-100B (opens in a new tab)

No sign-in needed.

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

How the S 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.34.9 / 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.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
Total89.9 / 10089 ptsExcellent (S) · 89 of 100 points: Model quality 34.9/45, Memory fit 25.0/25, 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 RTX PRO 6000 Blackwell

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 48.1 GB 96.0 GB 151 tok/s Runs well 76 ptsGreat (A) · 76 of 100 points: Model quality 25.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.6/15 GGUF (opens in a new tab)
Q3_K_M 57.3 GB 96.0 GB 118 tok/s Runs well 84 ptsGreat (A) · 84 of 100 points: Model quality 30.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.8/15 GGUF (opens in a new tab)
Q4_K_M 69.6 GB 96.0 GB 91.7 tok/s Runs well 89 ptsExcellent (S) · 89 of 100 points: Model quality 34.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 79.9 GB 96.0 GB 75.7 tok/s Runs well 89 ptsExcellent (S) · 89 of 100 points: Model quality 34.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 91.2 GB 96.0 GB 63.6 tok/s Tight fit 89 ptsExcellent (S) · 89 of 100 points: Model quality 35.6/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 116 GB 96.0 GB 8.2 tok/s Runs with offload 67 ptsGood (B) · 67 of 100 points: Model quality 36.0/45, Memory fit 10.0/25, Speed 6.8/15, Memory use 15.0/15 GGUF files (opens in a new tab)
F16 212 GB 96.0 GB — Won't run 10 ptsWon't run (F) · 10 of 100 points: Model quality 10.8/45, 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.