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Can the Intel Arc Pro B50 run Apertus v1.5 8B?

67 ptsGood (B) · 67 of 100 points: Model quality 19.7/45, Memory fit 25.0/25, Speed 11.5/15, Memory use 11.4/15

Yes, it runs well

Runs well. At Q4_K_M it needs about 7.9 GB of 16.0 GB usable, with an estimated speed of 23.6 tok/s.

Best quant
Q4_K_M
Generation speed
23.6 tok/s
Prompt processing
859 tok/s
First token (1K prompt)
1.2 s
Largest safe context
68K

Download Apertus v1.5 8B

Original weights · Hugging Face

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

huggingface.co/swiss-ai/Apertus-v1.5-8B (opens in a new tab)

Free Hugging Face account needed: accept the license on the model page and the download starts right away.

Links checked 7 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.19.7 / 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.11.5 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.11.4 / 15
Total67.6 / 10067 ptsGood (B) · 67 of 100 points: Model quality 19.7/45, Memory fit 25.0/25, Speed 11.5/15, Memory use 11.4/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the Intel Arc Pro B50

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 6.0 GB 16.0 GB 38.8 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 14.5/45, Memory fit 25.0/25, Speed 14.8/15, Memory use 8.7/15 All files (opens in a new tab)
Q3_K_M 6.8 GB 16.0 GB 30.5 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 17.4/45, Memory fit 25.0/25, Speed 13.1/15, Memory use 9.9/15 All files (opens in a new tab)
Q4_K_M 7.9 GB 16.0 GB 23.6 tok/s Runs well 67 ptsGood (B) · 67 of 100 points: Model quality 19.7/45, Memory fit 25.0/25, Speed 11.5/15, Memory use 11.4/15 All files (opens in a new tab)
Q5_K_M 8.8 GB 16.0 GB 19.5 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 20.1/45, Memory fit 25.0/25, Speed 10.5/15, Memory use 12.7/15 All files (opens in a new tab)
Q6_K 9.8 GB 16.0 GB 16.4 tok/s Runs well 69 ptsGood (B) · 69 of 100 points: Model quality 20.5/45, Memory fit 25.0/25, Speed 9.6/15, Memory use 14.1/15 All files (opens in a new tab)
Q8_0 11.9 GB 16.0 GB 12.0 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 20.7/45, Memory fit 25.0/25, Speed 8.2/15, Memory use 15.0/15 All files (opens in a new tab)
F16 20.3 GB 16.0 GB 4.0 tok/s Runs with offload 50 ptsUsable (C) · 50 of 100 points: Model quality 20.7/45, Memory fit 10.0/25, Speed 4.8/15, 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.