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Can the Intel Arc Pro B50 run Phi-3 Medium (14B)?

68 ptsGood (B) · 68 of 100 points: Model quality 19.7/45, Memory fit 25.0/25, Speed 9.2/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q4_K_M it needs about 10.8 GB of 16.0 GB usable, with an estimated speed of 15.0 tok/s.

Best quant
Q4_K_M
Generation speed
15.0 tok/s
Prompt processing
548 tok/s
First token (1K prompt)
1.9 s
Largest safe context
4K

Download Phi-3 Medium (14B)

Recommended for the Intel Arc Pro B50: Q4_K_M. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run phi3:14b-medium-4k-instruct-q4_K_M

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Quantized files · GGUF

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

Download the Q4_K_M file (opens in a new tab)

huggingface.co/bartowski/Phi-3-medium-4k-instruct-GGUF (opens in a new tab)

ollama run hf.co/bartowski/Phi-3-medium-4k-instruct-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/microsoft/Phi-3-medium-4k-instruct (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Microsoft. The weights themselves download from Hugging Face.

github.com/microsoft/PhiCookBook (opens in a new tab)

git clone https://github.com/microsoft/PhiCookBook

Microsoft’s official Phi cookbook: setup, inference and fine-tuning guides.

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.9.2 / 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
Total68.9 / 10068 ptsGood (B) · 68 of 100 points: Model quality 19.7/45, Memory fit 25.0/25, Speed 9.2/15, 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 7.8 GB 16.0 GB 24.7 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 14.5/45, Memory fit 25.0/25, Speed 11.8/15, Memory use 11.3/15 GGUF (opens in a new tab)
Q3_K_M 9.1 GB 16.0 GB 19.5 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 17.4/45, Memory fit 25.0/25, Speed 10.5/15, Memory use 13.1/15 GGUF (opens in a new tab)
Q4_K_M 10.8 GB 16.0 GB 15.0 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 19.7/45, Memory fit 25.0/25, Speed 9.2/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 12.2 GB 16.0 GB 12.4 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 20.1/45, Memory fit 25.0/25, Speed 8.4/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 13.7 GB 16.0 GB 10.4 tok/s Tight fit 67 ptsGood (B) · 67 of 100 points: Model quality 20.5/45, Memory fit 24.0/25, Speed 7.7/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 17.0 GB 16.0 GB 6.9 tok/s Runs with offload 51 ptsUsable (C) · 51 of 100 points: Model quality 20.7/45, Memory fit 10.0/25, Speed 6.2/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 30.2 GB 16.0 GB 2.0 tok/s Heavy offload 43 ptsUsable (C) · 43 of 100 points: Model quality 20.7/45, Memory fit 4.0/25, Speed 3.3/15, Memory use 15.0/15 Original weights (opens in a new tab)

Memory figures assume 4K 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.