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Can the Intel Arc B580 run Trinity Mini (26B-A3B)?

74 ptsGreat (A) · 74 of 100 points: Model quality 20.8/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q2_K it needs about 11.8 GB of 12.0 GB usable, with an estimated speed of 117 tok/s.

Best quant
Q2_K
Generation speed
117 tok/s
Prompt processing
5,005 tok/s
First token (1K prompt)
0.3 s
Largest safe context
16K

Download Trinity Mini (26B-A3B)

Recommended for the Intel Arc B580: 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/arcee-ai/Trinity-Mini-GGUF (opens in a new tab)

ollama run hf.co/arcee-ai/Trinity-Mini-GGUF:Q2_K

Original weights · Hugging Face

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

huggingface.co/arcee-ai/Trinity-Mini (opens in a new tab)

No sign-in needed.

Links checked 5 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.20.8 / 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.15.0 / 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
Total74.8 / 10074 ptsGreat (A) · 74 of 100 points: Model quality 20.8/45, Memory fit 24.0/25, Speed 15.0/15, 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 B580

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 11.8 GB 12.0 GB 117 tok/s Tight fit 74 ptsGreat (A) · 74 of 100 points: Model quality 20.8/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q3_K_M 14.2 GB 12.0 GB 53.7 tok/s Runs with offload 64 ptsGood (B) · 64 of 100 points: Model quality 24.9/45, Memory fit 10.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 17.3 GB 12.0 GB 29.6 tok/s Heavy offload 60 ptsGood (B) · 60 of 100 points: Model quality 28.2/45, Memory fit 4.0/25, Speed 12.9/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 19.9 GB 12.0 GB 21.0 tok/s Heavy offload 58 ptsGood (B) · 58 of 100 points: Model quality 28.8/45, Memory fit 4.0/25, Speed 10.9/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 22.8 GB 12.0 GB 15.8 tok/s Heavy offload 57 ptsUsable (C) · 57 of 100 points: Model quality 29.4/45, Memory fit 4.0/25, Speed 9.4/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 29.1 GB 12.0 GB 10.1 tok/s Heavy offload 56 ptsUsable (C) · 56 of 100 points: Model quality 29.7/45, Memory fit 4.0/25, Speed 7.5/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 53.6 GB 12.0 GB — Won't run 8 ptsWon't run (F) · 8 of 100 points: Model quality 8.9/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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.