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Can the Intel Arc Pro B50 run MedGemma 27B Text IT?

72 ptsGreat (A) · 72 of 100 points: Model quality 26.5/45, Memory fit 24.0/25, Speed 7.5/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q3_K_M it needs about 15.4 GB of 16.0 GB usable, with an estimated speed of 10.1 tok/s.

Best quant
Q3_K_M
Generation speed
10.1 tok/s
Prompt processing
283 tok/s
First token (1K prompt)
3.6 s
Largest safe context
13K

Download MedGemma 27B Text IT

Recommended for the Intel Arc Pro B50: Q3_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 Q3_K_M file (opens in a new tab)

huggingface.co/unsloth/medgemma-27b-text-it-GGUF (opens in a new tab)

ollama run hf.co/unsloth/medgemma-27b-text-it-GGUF:Q3_K_M

Original weights · Hugging Face

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

huggingface.co/google/medgemma-27b-text-it (opens in a new tab)

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

How the A 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.26.5 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.7.5 / 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
Total73.0 / 10072 ptsGreat (A) · 72 of 100 points: Model quality 26.5/45, Memory fit 24.0/25, Speed 7.5/15, 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 Pro B50

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 13.0 GB 16.0 GB 12.8 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 22.1/45, Memory fit 25.0/25, Speed 8.5/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q3_K_M 15.4 GB 16.0 GB 10.1 tok/s Tight fit 72 ptsGreat (A) · 72 of 100 points: Model quality 26.5/45, Memory fit 24.0/25, Speed 7.5/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 18.7 GB 16.0 GB 6.2 tok/s Runs with offload 60 ptsGood (B) · 60 of 100 points: Model quality 29.9/45, Memory fit 10.0/25, Speed 5.9/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 21.4 GB 16.0 GB 4.5 tok/s Runs with offload 60 ptsGood (B) · 60 of 100 points: Model quality 30.6/45, Memory fit 10.0/25, Speed 5.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 24.4 GB 16.0 GB 3.4 tok/s Heavy offload 54 ptsUsable (C) · 54 of 100 points: Model quality 31.2/45, Memory fit 4.0/25, Speed 4.4/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 30.8 GB 16.0 GB 2.2 tok/s Heavy offload 54 ptsUsable (C) · 54 of 100 points: Model quality 31.5/45, Memory fit 4.0/25, Speed 3.5/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 56.2 GB 16.0 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 9.5/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (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.