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Can the Intel Arc A380 run Ministral 8B (2410)?

67 ptsGood (B) · 67 of 100 points: Model quality 14.5/45, Memory fit 24.0/25, Speed 14.2/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q2_K it needs about 5.3 GB of 6.0 GB usable, with an estimated speed of 35.7 tok/s.

Best quant
Q2_K
Generation speed
35.7 tok/s
Prompt processing
562 tok/s
First token (1K prompt)
1.8 s
Largest safe context
12K

Download Ministral 8B (2410)

Recommended for the Intel Arc A380: Q2_K. 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 Q2_K file (opens in a new tab)

huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF (opens in a new tab)

ollama run hf.co/bartowski/Ministral-8B-Instruct-2410-GGUF:Q2_K

Original weights · Hugging Face

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

huggingface.co/mistralai/Ministral-8B-Instruct-2410 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/mistralai/mistral-inference (opens in a new tab)

git clone https://github.com/mistralai/mistral-inference

Mistral AI’s official inference code.

Links checked 6 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.14.5 / 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.14.2 / 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
Total67.7 / 10067 ptsGood (B) · 67 of 100 points: Model quality 14.5/45, Memory fit 24.0/25, Speed 14.2/15, 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 A380

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 5.3 GB 6.0 GB 35.7 tok/s Tight fit 67 ptsGood (B) · 67 of 100 points: Model quality 14.5/45, Memory fit 24.0/25, Speed 14.2/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q3_K_M 6.0 GB 6.0 GB 27.8 tok/s Runs with offload 54 ptsUsable (C) · 54 of 100 points: Model quality 17.4/45, Memory fit 10.0/25, Speed 12.5/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 7.0 GB 6.0 GB 17.7 tok/s Runs with offload 54 ptsUsable (C) · 54 of 100 points: Model quality 19.7/45, Memory fit 10.0/25, Speed 10.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 7.8 GB 6.0 GB 13.2 tok/s Heavy offload 47 ptsUsable (C) · 47 of 100 points: Model quality 20.1/45, Memory fit 4.0/25, Speed 8.6/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 8.7 GB 6.0 GB 10.3 tok/s Heavy offload 47 ptsUsable (C) · 47 of 100 points: Model quality 20.5/45, Memory fit 4.0/25, Speed 7.6/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 10.6 GB 6.0 GB 6.9 tok/s Heavy offload 45 ptsUsable (C) · 45 of 100 points: Model quality 20.7/45, Memory fit 4.0/25, Speed 6.2/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 18.1 GB 6.0 GB — Won't run 6 ptsWon't run (F) · 6 of 100 points: Model quality 6.2/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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

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.