MedGemma 27B Text IT on Intel Arc Pro B50
Can the Intel Arc Pro B50 run MedGemma 27B Text IT?
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_MOriginal 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)
How the A grade is scored
Every result gets up to 100 points from four parts. The points decide the letter grade.
| Model quality | 26.5 / 45 | |
|---|---|---|
| Memory fit | 24.0 / 25 | |
| Speed | 7.5 / 15 | |
| Memory use | 15.0 / 15 | |
| Total | 73.0 / 100 | 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 |
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
- 86 ptsExcellent (S) · 86 of 100 points: Model quality 31.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5090
- 84 ptsGreat (A) · 84 of 100 points: Model quality 29.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 3090
- 84 ptsGreat (A) · 84 of 100 points: Model quality 29.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4090
- 84 ptsGreat (A) · 84 of 100 points: Model quality 29.9/45, Memory fit 25.0/25, Speed 14.5/15, Memory use 15.0/15 AMD Radeon RX 7900 XTX
- 80 ptsGreat (A) · 80 of 100 points: Model quality 26.5/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4080 SUPER
- 80 ptsGreat (A) · 80 of 100 points: Model quality 26.5/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5070 Ti
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.