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Can the Intel Arc B580 run UI-Venus 2 9B?

85 ptsExcellent (S) · 85 of 100 points: Model quality 30.6/45, Memory fit 25.0/25, Speed 14.5/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q5_K_M it needs about 8.1 GB of 12.0 GB usable, with an estimated speed of 37.5 tok/s.

Best quant
Q5_K_M
Generation speed
37.5 tok/s
Prompt processing
1,596 tok/s
First token (1K prompt)
0.6 s
Largest safe context
122K

Download UI-Venus 2 9B

Recommended for the Intel Arc B580: 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/bartowski/UI-Venus-2-9B-GGUF (opens in a new tab)

ollama run hf.co/bartowski/UI-Venus-2-9B-GGUF:Q5_K_M

Original weights · Hugging Face

Full-precision files from Ant Group (inclusionAI), for fine-tuning, vLLM, Transformers.

huggingface.co/inclusionAI/UI-Venus-2-9B (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.30.6 / 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.14.5 / 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
Total85.1 / 10085 ptsExcellent (S) · 85 of 100 points: Model quality 30.6/45, Memory fit 25.0/25, Speed 14.5/15, 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 Intel Arc B580

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 5.2 GB 12.0 GB 74.6 tok/s Runs well 72 ptsGreat (A) · 72 of 100 points: Model quality 22.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.1/15 GGUF (opens in a new tab)
Q3_K_M 6.1 GB 12.0 GB 58.7 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 26.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.7/15 GGUF (opens in a new tab)
Q4_K_M 7.2 GB 12.0 GB 45.4 tok/s Runs well 83 ptsGreat (A) · 83 of 100 points: Model quality 29.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.9/15 GGUF (opens in a new tab)
Q5_K_M 8.1 GB 12.0 GB 37.5 tok/s Runs well 85 ptsExcellent (S) · 85 of 100 points: Model quality 30.6/45, Memory fit 25.0/25, Speed 14.5/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 9.2 GB 12.0 GB 31.5 tok/s Runs well 84 ptsGreat (A) · 84 of 100 points: Model quality 31.2/45, Memory fit 25.0/25, Speed 13.3/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 11.4 GB 12.0 GB 23.1 tok/s Tight fit 81 ptsGreat (A) · 81 of 100 points: Model quality 31.5/45, Memory fit 24.0/25, Speed 11.4/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 20.3 GB 12.0 GB 3.7 tok/s Heavy offload 55 ptsUsable (C) · 55 of 100 points: Model quality 31.5/45, Memory fit 4.0/25, Speed 4.6/15, Memory use 15.0/15 GGUF (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.