Check my GPU

on

Can the AMD Radeon RX 9070 XT run Qwen2.5-Omni 3B?

64 ptsGood (B) · 64 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.9/15

Yes, it runs well

Runs well. At Q8_0 it needs about 7.6 GB of 16.0 GB usable, with an estimated speed of 62.2 tok/s.

Best quant
Q8_0
Generation speed
62.2 tok/s
Prompt processing
10,889 tok/s
First token (1K prompt)
0.3 s
Largest safe context
32K

Download Qwen2.5-Omni 3B

Recommended for the AMD Radeon RX 9070 XT: Q8_0. 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 Q8_0 file (opens in a new tab)

huggingface.co/unsloth/Qwen2.5-Omni-3B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Qwen2.5-Omni-3B-GGUF:Q8_0

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen2.5-Omni-3B (opens in a new tab)

No sign-in needed.

Links checked 7 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.13.5 / 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.10.9 / 15
Total64.4 / 10064 ptsGood (B) · 64 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.9/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the AMD Radeon RX 9070 XT

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 3.9 GB 16.0 GB 201 tok/s Runs well 55 ptsUsable (C) · 55 of 100 points: Model quality 9.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.7/15 GGUF (opens in a new tab)
Q3_K_M 4.4 GB 16.0 GB 158 tok/s Runs well 57 ptsUsable (C) · 57 of 100 points: Model quality 11.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.4/15 GGUF (opens in a new tab)
Q4_K_M 5.1 GB 16.0 GB 122 tok/s Runs well 60 ptsGood (B) · 60 of 100 points: Model quality 12.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.3/15 GGUF (opens in a new tab)
Q5_K_M 5.6 GB 16.0 GB 101 tok/s Runs well 61 ptsGood (B) · 61 of 100 points: Model quality 13.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.1/15 GGUF (opens in a new tab)
Q6_K 6.2 GB 16.0 GB 84.7 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 13.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.0/15 GGUF (opens in a new tab)
Q8_0 7.6 GB 16.0 GB 62.2 tok/s Runs well 64 ptsGood (B) · 64 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.9/15 GGUF (opens in a new tab)
F16 12.8 GB 16.0 GB 28.6 tok/s Runs well 66 ptsGood (B) · 66 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 12.7/15, 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.