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Can the Intel Arc A380 run EXAONE 3.0 7.8B Instruct?

66 ptsGood (B) · 66 of 100 points: Model quality 12.0/45, Memory fit 25.0/25, Speed 14.4/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q2_K it needs about 4.6 GB of 6.0 GB usable, with an estimated speed of 36.6 tok/s.

Best quant
Q2_K
Generation speed
36.6 tok/s
Prompt processing
577 tok/s
First token (1K prompt)
1.8 s
Largest safe context
4K

Download EXAONE 3.0 7.8B Instruct

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/EXAONE-3.0-7.8B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/bartowski/EXAONE-3.0-7.8B-Instruct-GGUF:Q2_K

Original weights · Hugging Face

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

huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct (opens in a new tab)

Free Hugging Face account needed: accept the license on the model page and the download starts right away.

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.12.0 / 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.14.4 / 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
Total66.3 / 10066 ptsGood (B) · 66 of 100 points: Model quality 12.0/45, Memory fit 25.0/25, Speed 14.4/15, 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 4.6 GB 6.0 GB 36.6 tok/s Runs well 66 ptsGood (B) · 66 of 100 points: Model quality 12.0/45, Memory fit 25.0/25, Speed 14.4/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q3_K_M 5.3 GB 6.0 GB 28.8 tok/s Tight fit 66 ptsGood (B) · 66 of 100 points: Model quality 14.4/45, Memory fit 24.0/25, Speed 12.7/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 6.2 GB 6.0 GB 21.3 tok/s Runs with offload 52 ptsUsable (C) · 52 of 100 points: Model quality 16.2/45, Memory fit 10.0/25, Speed 10.9/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 7.0 GB 6.0 GB 15.3 tok/s Runs with offload 50 ptsUsable (C) · 50 of 100 points: Model quality 16.6/45, Memory fit 10.0/25, Speed 9.3/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 7.8 GB 6.0 GB 11.7 tok/s Runs with offload 50 ptsUsable (C) · 50 of 100 points: Model quality 16.9/45, Memory fit 10.0/25, Speed 8.1/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 9.7 GB 6.0 GB 7.5 tok/s Heavy offload 42 ptsUsable (C) · 42 of 100 points: Model quality 17.1/45, Memory fit 4.0/25, Speed 6.5/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 17.1 GB 6.0 GB 2.9 tok/s Heavy offload 40 ptsPoor (D) · 40 of 100 points: Model quality 17.1/45, Memory fit 4.0/25, Speed 4.0/15, 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.