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Can the Intel Arc A380 run Mathstral 7B?

65 ptsGood (B) · 65 of 100 points: Model quality 10.7/45, Memory fit 25.0/25, Speed 14.9/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q2_K it needs about 4.9 GB of 6.0 GB usable, with an estimated speed of 39.5 tok/s.

Best quant
Q2_K
Generation speed
39.5 tok/s
Prompt processing
622 tok/s
First token (1K prompt)
1.6 s
Largest safe context
16K

Download Mathstral 7B

Recommended for the Intel Arc A380: Q2_K. 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 mathstral:7b-v0.1-q2_K

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 Q2_K file (opens in a new tab)

huggingface.co/bartowski/mathstral-7B-v0.1-GGUF (opens in a new tab)

ollama run hf.co/bartowski/mathstral-7B-v0.1-GGUF:Q2_K

Original weights · Hugging Face

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

huggingface.co/mistralai/Mathstral-7B-v0.1 (opens in a new tab)

No sign-in needed.

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.10.7 / 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.14.9 / 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
Total65.6 / 10065 ptsGood (B) · 65 of 100 points: Model quality 10.7/45, Memory fit 25.0/25, Speed 14.9/15, 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 4.9 GB 6.0 GB 39.5 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 10.7/45, Memory fit 25.0/25, Speed 14.9/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q3_K_M 5.5 GB 6.0 GB 31.1 tok/s Tight fit 65 ptsGood (B) · 65 of 100 points: Model quality 12.9/45, Memory fit 24.0/25, Speed 13.2/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 6.4 GB 6.0 GB 21.8 tok/s Runs with offload 50 ptsUsable (C) · 50 of 100 points: Model quality 14.5/45, Memory fit 10.0/25, Speed 11.1/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 7.1 GB 6.0 GB 15.9 tok/s Runs with offload 49 ptsUsable (C) · 49 of 100 points: Model quality 14.8/45, Memory fit 10.0/25, Speed 9.5/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 7.9 GB 6.0 GB 12.2 tok/s Heavy offload 42 ptsUsable (C) · 42 of 100 points: Model quality 15.1/45, Memory fit 4.0/25, Speed 8.3/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 9.7 GB 6.0 GB 8.0 tok/s Heavy offload 40 ptsPoor (D) · 40 of 100 points: Model quality 15.3/45, Memory fit 4.0/25, Speed 6.7/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 16.5 GB 6.0 GB 3.1 tok/s Heavy offload 38 ptsPoor (D) · 38 of 100 points: Model quality 15.3/45, Memory fit 4.0/25, Speed 4.2/15, Memory use 15.0/15 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.