DiffusionGemma 26B-A4B IT on Intel Arc B580

Can the Intel Arc B580 run DiffusionGemma 26B-A4B IT?

Yes, it runs well (tight fit)

Runs well (tight fit). At Q2_K it needs about 11.9 GB of 12.0 GB usable, with an estimated speed of 113 tok/s.

Best quant: Q2_K
Generation speed: 113 tok/s
Prompt processing: 3,754 tok/s
First token (1K prompt): 0.3 s
Largest safe context: 9K

Best app to run it on the Intel Arc B580

On Windows

llama.cpp

Command line + local API · runs on llama.cpp

Supports Intel Arc through SYCL and Vulkan.
Usually the first to support new model architectures.

llama-server -hf unsloth/diffusiongemma-26B-A4B-it-GGUF:Q2_K

LM Studio Point-and-click: search, download and chat in one window.
Jan Point-and-click: search, download and chat in one window.

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

Supports Intel Arc through SYCL and Vulkan.
Usually the first to support new model architectures.

llama-server -hf unsloth/diffusiongemma-26B-A4B-it-GGUF:Q2_K

LM Studio Point-and-click: search, download and chat in one window.
Jan Point-and-click: search, download and chat in one window.

Want an agent? DiffusionGemma 26B-A4B IT supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download DiffusionGemma 26B-A4B IT

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/unsloth/diffusiongemma-26B-A4B-it-GGUF (opens in a new tab)

ollama run hf.co/unsloth/diffusiongemma-26B-A4B-it-GGUF

Original weights · Hugging Face

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

huggingface.co/google/diffusiongemma-26B-A4B-it (opens in a new tab)

No sign-in needed.

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 54Fair quality (C) · 54 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q2_K, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs welltight fit: How well it runs on the Intel Arc B580: speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	24.6 / 45
Memory fitFull marks when everything sits in GPU 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
Match score	78.6 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the Intel Arc B580

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	11.9 GB	12.0 GB	113 tok/s	Runs welltight fit	54Fair quality (C) · 54 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q3_K_M`	14.3 GB	12.0 GB	62.8 tok/s	Runs welloffloads to RAM	65Good quality (B) · 65 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q4_K_M`	17.4 GB	12.0 GB	38.2 tok/s	Slowheavy RAM offload	74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	19.9 GB	12.0 GB	28.1 tok/s	Slowheavy RAM offload	75Great quality (A) · 75 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	22.8 GB	12.0 GB	21.7 tok/s	Slowheavy RAM offload	77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	29.0 GB	12.0 GB	14.2 tok/s	Slowheavy RAM offload	78Great quality (A) · 78 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	53.2 GB	12.0 GB	—	Won't run	78Great quality (A) · 78 of 100: how capable the model is at F16, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking.	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

78Great quality (A) · 78 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. Apple M1 Max (64 GB) Q8_0 · 44.7 tok/s · Runs great
78Great quality (A) · 78 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Max (64 GB) Q8_0 · 57.4 tok/s · Runs great
77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Pro (48 GB) Q6_K · 42.4 tok/s · Runs great
77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 Q6_K · 161 tok/s · Runs great
78Great quality (A) · 78 of 100: how capable the model is at F16, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell F16 · 82.5 tok/s · Runs great
77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX Q6_K · 108 tok/s · Runs well (tight fit)

Quick answers

How much memory does DiffusionGemma 26B-A4B IT need on the Arc B580 12GB?

At Q2_K it needs about 11.9 GB. The Arc B580 12GB has 12.0 GB usable.

How fast is DiffusionGemma 26B-A4B IT on the Arc B580 12GB?

About 113 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of DiffusionGemma 26B-A4B IT should I use on the Arc B580 12GB?

Q2_K. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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.