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Can the Intel Arc A380 run CodeGemma 2B?

58 ptsGood (B) · 58 of 100 points: Model quality 6.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.0/15

Yes, it runs well

Runs well. At Q6_K it needs about 3.1 GB of 6.0 GB usable, with an estimated speed of 48.2 tok/s.

Best quant
Q6_K
Generation speed
48.2 tok/s
Prompt processing
1,797 tok/s
First token (1K prompt)
0.6 s
Largest safe context
8K

Download CodeGemma 2B

Recommended for the Intel Arc A380: Q6_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 codegemma:2b-code-q6_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.

huggingface.co/google/codegemma-2b-GGUF (opens in a new tab)

ollama run hf.co/google/codegemma-2b-GGUF

Original weights · Hugging Face

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

huggingface.co/google/codegemma-2b (opens in a new tab)

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

Links checked 5 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.6.2 / 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.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.12.0 / 15
Total58.2 / 10058 ptsGood (B) · 58 of 100 points: Model quality 6.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.0/15

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 2.1 GB 6.0 GB 114 tok/s Runs well 52 ptsUsable (C) · 52 of 100 points: Model quality 4.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.9/15
Q3_K_M 2.3 GB 6.0 GB 89.8 tok/s Runs well 54 ptsUsable (C) · 54 of 100 points: Model quality 5.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.8/15
Q4_K_M 2.6 GB 6.0 GB 69.5 tok/s Runs well 55 ptsUsable (C) · 55 of 100 points: Model quality 6.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.9/15
Q5_K_M 2.8 GB 6.0 GB 57.4 tok/s Runs well 57 ptsUsable (C) · 57 of 100 points: Model quality 6.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.9/15
Q6_K 3.1 GB 6.0 GB 48.2 tok/s Runs well 58 ptsGood (B) · 58 of 100 points: Model quality 6.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.0/15
Q8_0 3.7 GB 6.0 GB 35.4 tok/s Runs well 59 ptsGood (B) · 59 of 100 points: Model quality 6.3/45, Memory fit 25.0/25, Speed 14.1/15, Memory use 14.3/15
F16 6.1 GB 6.0 GB 16.0 tok/s Runs with offload 40 ptsPoor (D) · 40 of 100 points: Model quality 6.3/45, Memory fit 10.0/25, Speed 9.5/15, 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.