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Can the Intel Arc A380 run Jamba2 3B?

71 ptsGood (B) · 71 of 100 points: Model quality 18.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.0/15

Yes, it runs well

Runs well. At Q6_K it needs about 3.4 GB of 6.0 GB usable, with an estimated speed of 39.9 tok/s.

Best quant
Q6_K
Generation speed
39.9 tok/s
Prompt processing
1,488 tok/s
First token (1K prompt)
0.7 s
Largest safe context
256K

Download Jamba2 3B

Recommended for the Intel Arc A380: Q6_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 Q6_K file (opens in a new tab)

huggingface.co/bartowski/ai21labs_AI21-Jamba2-3B-GGUF (opens in a new tab)

ollama run hf.co/bartowski/ai21labs_AI21-Jamba2-3B-GGUF:Q6_K

Original weights · Hugging Face

Full-precision files from AI21 Labs, for fine-tuning, vLLM, Transformers.

huggingface.co/ai21labs/AI21-Jamba2-3B (opens in a new tab)

No sign-in needed.

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.18.7 / 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.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.13.0 / 15
Total71.7 / 10071 ptsGood (B) · 71 of 100 points: Model quality 18.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.0/15

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 94.5 tok/s Runs well 61 ptsGood (B) · 61 of 100 points: Model quality 13.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.2/15 GGUF (opens in a new tab)
Q3_K_M 2.4 GB 6.0 GB 74.4 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 15.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.2/15 GGUF (opens in a new tab)
Q4_K_M 2.8 GB 6.0 GB 57.6 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 18.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.6/15 GGUF (opens in a new tab)
Q5_K_M 3.1 GB 6.0 GB 47.6 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 18.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.8/15 GGUF (opens in a new tab)
Q6_K 3.4 GB 6.0 GB 39.9 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 18.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.0/15 GGUF (opens in a new tab)
Q8_0 4.1 GB 6.0 GB 29.3 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 18.9/45, Memory fit 25.0/25, Speed 12.8/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 7.0 GB 6.0 GB 11.4 tok/s Runs with offload 51 ptsUsable (C) · 51 of 100 points: Model quality 18.9/45, Memory fit 10.0/25, Speed 8.0/15, 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.