Llama 3.1 8B Instruct on Intel Arc Pro B50

Can the Intel Arc Pro B50 run Llama 3.1 8B Instruct?

68 pts

Yes, it runs well

Runs well. At Q4_K_M it needs about 7.4 GB of 16.0 GB usable, with an estimated speed of 26.2 tok/s.

Best quant: Q4_K_M
Generation speed: 26.2 tok/s
Prompt processing: 952 tok/s
First token (1K prompt): 1.1 s
Largest safe context: 72K

Download Llama 3.1 8B Instruct

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 llama3.1:8b-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/unsloth/Llama-3.1-8B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Llama-3.1-8B-Instruct-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/meta-llama/Llama-3.1-8B-Instruct (opens in a new tab)

Free Hugging Face account needed: request access on the model page, and Meta approves it (usually within minutes to a few hours).

Official repo · GitHub

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

github.com/meta-llama/llama-models (opens in a new tab)

git clone https://github.com/meta-llama/llama-models

Meta’s official Llama repo: model cards, prompt formats and download tools.

Links checked 1 week 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.	20.5 / 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.	12.1 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	10.6 / 15
Total	68.3 / 100	68 ptsGood (B) · 68 of 100 points: Model quality 20.5/45, Memory fit 25.0/25, Speed 12.1/15, Memory use 10.6/15

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`	5.7 GB	16.0 GB	43.0 tok/s	Runs well	63 ptsGood (B) · 63 of 100 points: Model quality 15.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.2/15	GGUF (opens in a new tab)
`Q3_K_M`	6.4 GB	16.0 GB	33.8 tok/s	Runs well	66 ptsGood (B) · 66 of 100 points: Model quality 18.1/45, Memory fit 25.0/25, Speed 13.8/15, Memory use 9.2/15	GGUF (opens in a new tab)
`Q4_K_M`	7.4 GB	16.0 GB	26.2 tok/s	Runs well	68 ptsGood (B) · 68 of 100 points: Model quality 20.5/45, Memory fit 25.0/25, Speed 12.1/15, Memory use 10.6/15	GGUF (opens in a new tab)
`Q5_K_M`	8.2 GB	16.0 GB	21.6 tok/s	Runs well	68 ptsGood (B) · 68 of 100 points: Model quality 21.0/45, Memory fit 25.0/25, Speed 11.0/15, Memory use 11.8/15	GGUF (opens in a new tab)
`Q6_K`	9.1 GB	16.0 GB	18.1 tok/s	Runs well	69 ptsGood (B) · 69 of 100 points: Model quality 21.4/45, Memory fit 25.0/25, Speed 10.1/15, Memory use 13.1/15	GGUF (opens in a new tab)
`Q8_0`	11.0 GB	16.0 GB	13.3 tok/s	Runs well	70 ptsGood (B) · 70 of 100 points: Model quality 21.6/45, Memory fit 25.0/25, Speed 8.7/15, Memory use 15.0/15	GGUF (UD-Q8_K_XL) (opens in a new tab)
`F16`	18.5 GB	16.0 GB	4.9 tok/s	Runs with offload	51 ptsUsable (C) · 51 of 100 points: Model quality 21.6/45, Memory fit 10.0/25, Speed 5.3/15, 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

76 ptsGreat (A) · 76 of 100 points: Model quality 21.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 AMD Radeon RX 9070 XT Q8_0 · 42.9 tok/s
76 ptsGreat (A) · 76 of 100 points: Model quality 21.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4070 Ti SUPER Q8_0 · 52.3 tok/s
76 ptsGreat (A) · 76 of 100 points: Model quality 21.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4080 SUPER Q8_0 · 57.3 tok/s
76 ptsGreat (A) · 76 of 100 points: Model quality 21.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5070 Ti Q8_0 · 69.7 tok/s
76 ptsGreat (A) · 76 of 100 points: Model quality 21.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5080 Q8_0 · 74.7 tok/s
76 ptsGreat (A) · 76 of 100 points: Model quality 21.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4070 SUPER Q6_K · 53.5 tok/s

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.