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Can the NVIDIA RTX PRO 6000 Blackwell run Sarvam 30B?

86 ptsExcellent (S) · 86 of 100 points: Model quality 31.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At F16 it needs about 69.9 GB of 96.0 GB usable, with an estimated speed of 108 tok/s.

Best quant
F16
Generation speed
108 tok/s
Prompt processing
37,500 tok/s
First token (1K prompt)
0.3 s
Largest safe context
128K

Download Sarvam 30B

Quantized files · GGUF

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

huggingface.co/sarvamai/sarvam-30b-gguf (opens in a new tab)

ollama run hf.co/sarvamai/sarvam-30b-gguf

Original weights · Hugging Face

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

huggingface.co/sarvamai/sarvam-30b (opens in a new tab)

No sign-in needed.

Links checked 7 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.31.5 / 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
Total86.5 / 10086 ptsExcellent (S) · 86 of 100 points: Model quality 31.5/45, 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 18.4 GB 96.0 GB 753 tok/s Runs well 66 ptsGood (B) · 66 of 100 points: Model quality 22.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.4/15 GGUF files (opens in a new tab)
Q3_K_M 21.3 GB 96.0 GB 592 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 26.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.1/15 GGUF files (opens in a new tab)
Q4_K_M 25.2 GB 96.0 GB 458 tok/s Runs well 75 ptsGreat (A) · 75 of 100 points: Model quality 29.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.1/15 GGUF · part 1 of 6 (opens in a new tab)
Q5_K_M 28.4 GB 96.0 GB 379 tok/s Runs well 77 ptsGreat (A) · 77 of 100 points: Model quality 30.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.8/15 GGUF files (opens in a new tab)
Q6_K 31.9 GB 96.0 GB 318 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 31.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.7/15 GGUF files (opens in a new tab)
Q8_0 39.6 GB 96.0 GB 233 tok/s Runs well 81 ptsGreat (A) · 81 of 100 points: Model quality 31.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.5/15 GGUF files (opens in a new tab)
F16 69.9 GB 96.0 GB 108 tok/s Runs well 86 ptsExcellent (S) · 86 of 100 points: Model quality 31.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 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.