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Can the NVIDIA RTX PRO 6000 Blackwell run Agents-A1 35B-A3B?

94 ptsExcellent (S) · 94 of 100 points: Model quality 39.2/45, 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 75.8 GB of 96.0 GB usable, with an estimated speed of 86.0 tok/s.

Best quant
F16
Generation speed
86.0 tok/s
Prompt processing
30,000 tok/s
First token (1K prompt)
0.3 s
Largest safe context
256K

Download Agents-A1 35B-A3B

Quantized files · GGUF

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

huggingface.co/mradermacher/Agents-A1-i1-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/Agents-A1-i1-GGUF

Original weights · Hugging Face

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

huggingface.co/InternScience/Agents-A1 (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.39.2 / 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
Total94.2 / 10094 ptsExcellent (S) · 94 of 100 points: Model quality 39.2/45, 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 19.6 GB 96.0 GB 602 tok/s Runs well 72 ptsGreat (A) · 72 of 100 points: Model quality 27.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.7/15 GGUF (opens in a new tab)
Q3_K_M 22.8 GB 96.0 GB 474 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 32.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.5/15 GGUF (opens in a new tab)
Q4_K_M 27.0 GB 96.0 GB 367 tok/s Runs well 83 ptsGreat (A) · 83 of 100 points: Model quality 37.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.5/15 GGUF (opens in a new tab)
Q5_K_M 30.5 GB 96.0 GB 303 tok/s Runs well 85 ptsExcellent (S) · 85 of 100 points: Model quality 38.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.3/15 GGUF (opens in a new tab)
Q6_K 34.4 GB 96.0 GB 254 tok/s Runs well 87 ptsExcellent (S) · 87 of 100 points: Model quality 38.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.3/15 GGUF (opens in a new tab)
Q8_0 42.8 GB 96.0 GB 187 tok/s Runs well 89 ptsExcellent (S) · 89 of 100 points: Model quality 39.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.3/15 GGUF files (opens in a new tab)
F16 75.8 GB 96.0 GB 86.0 tok/s Runs well 94 ptsExcellent (S) · 94 of 100 points: Model quality 39.2/45, 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.