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Can the Intel Arc A380 run InternVL3 8B?

72 ptsGreat (A) · 72 of 100 points: Model quality 21.2/45, Memory fit 24.0/25, Speed 12.6/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q3_K_M it needs about 5.3 GB of 6.0 GB usable, with an estimated speed of 28.4 tok/s.

Best quant
Q3_K_M
Generation speed
28.4 tok/s
Prompt processing
568 tok/s
First token (1K prompt)
1.8 s
Largest safe context
20K

Download InternVL3 8B

Recommended for the Intel Arc A380: 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/InternVL3-8B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/InternVL3-8B-GGUF:Q3_K_M

Original weights · Hugging Face

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

huggingface.co/OpenGVLab/InternVL3-8B (opens in a new tab)

No sign-in needed.

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

How the A 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.21.2 / 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.12.6 / 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
Total72.8 / 10072 ptsGreat (A) · 72 of 100 points: Model quality 21.2/45, Memory fit 24.0/25, Speed 12.6/15, 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 4.5 GB 6.0 GB 36.1 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 17.6/45, Memory fit 25.0/25, Speed 14.2/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q3_K_M 5.3 GB 6.0 GB 28.4 tok/s Tight fit 72 ptsGreat (A) · 72 of 100 points: Model quality 21.2/45, Memory fit 24.0/25, Speed 12.6/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 6.2 GB 6.0 GB 20.9 tok/s Runs with offload 59 ptsGood (B) · 59 of 100 points: Model quality 23.9/45, Memory fit 10.0/25, Speed 10.8/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 7.0 GB 6.0 GB 15.1 tok/s Runs with offload 58 ptsGood (B) · 58 of 100 points: Model quality 24.4/45, Memory fit 10.0/25, Speed 9.2/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 7.9 GB 6.0 GB 11.5 tok/s Runs with offload 57 ptsUsable (C) · 57 of 100 points: Model quality 24.9/45, Memory fit 10.0/25, Speed 8.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 9.8 GB 6.0 GB 7.4 tok/s Heavy offload 50 ptsUsable (C) · 50 of 100 points: Model quality 25.2/45, Memory fit 4.0/25, Speed 6.5/15, Memory use 15.0/15 GGUF (UD-Q8_K_XL) (opens in a new tab)
F16 17.2 GB 6.0 GB 2.9 tok/s Heavy offload 48 ptsUsable (C) · 48 of 100 points: Model quality 25.2/45, Memory fit 4.0/25, Speed 4.0/15, 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

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.