A 16GB AMD graphics card ran a 27B Qwen model at about 36 tokens a second
A hands-on test put a 27-billion-parameter vision model on an AMD RX 9070 XT using point-and-click software, showing local AI is not only for Nvidia owners.
The short version
- Card: PowerColor RX 9070 XT Dark Dog, 16GB of video memory, running Windows with LM Studio
- Model: Qwen3.8-27B with vision, in a 10.1GB IQ3_XXS file plus a roughly 931MB image component
- Speed: 36.57 tokens per second, with the first output after about 4.61 seconds
- Settings: 128K context with a compressed q4_0 cache, using about 13.5 to 14GB of the 16GB
- The test was one run by the outlet, which wrote a snake game that played correctly
Why it matters
Most talk about running AI at home assumes an Nvidia card. This test suggests a 16GB AMD card can handle a large model with image input, provided you pick a file size that fits its memory. It is one outlet's single run, so treat the speed as a data point rather than a benchmark.
What the test showed
As China.com reported, the outlet ran Qwen3.8-27B on a PowerColor Radeon RX 9070 XT Dark Dog with 16GB of video memory. The model has 27 billion parameters (the adjustable numbers that make up its knowledge) and can also interpret images. The name is printed as 'Qwen3.8' in the source, and we repeat it as written.
The software was LM Studio, a free desktop app on Windows. According to the report, it detects the AMD card on its own and picks the right acceleration, so setup is done by clicking rather than coding.
Choosing a file that fits
Open models usually come in quantized versions, meaning compressed copies that trade some quality for a smaller size. The outlet says a common 4-bit version of this model is about 16GB, which would fill the card and push work into slower system memory during long chats. A 2-bit version, around 8 to 9GB, leaves plenty of room but loses some reasoning and language quality.
It settled on a middle option called IQ3_XXS, at about 10.1GB. That left space for a roughly 931MB add-on that lets the model look at images, plus room for memory of the conversation.
Settings and results
The model supports up to 256K tokens of context (how much text it can keep in mind), but the outlet says maxing that out would nearly exhaust the card. It used 128K instead, with a compressed cache setting called q4_0 that needs about 2.2GB. Total use came to about 13.5 to 14GB, leaving 1.5 to 2GB spare. All model layers ran on the graphics card.
The test prompt asked for a snake game in HTML. The first output appeared after about 4.61 seconds, and generation held at 36.57 tokens per second. A token is a small chunk of text, roughly a word or part of one.
The model produced 19,344 tokens over about 533 seconds, finished normally and did not crash. The outlet says the resulting game worked, with responsive controls, scoring, speed-ups, wall collisions and sound effects.
Caveats
This is one outlet's single run on one card, with a coding prompt. The piece also promotes the PowerColor card's cooling design, so read it partly as hardware marketing. It gives no comparison with an Nvidia card or with other file sizes.
What to watch
- Whether other outlets or users reproduce this speed on AMD cards with different software
- How the IQ3_XXS compression affects answer quality compared with the 4-bit version
Try it
Before downloading any model, compare the file size with your card's memory and leave a few GB spare for the image add-on and conversation memory, as this test did.
Sources and links
- ReportingChina.com: the article we sourced
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Listen to the daily showsFirst reported by China.com · Read the original. Written by the SI Agent News Desk from the reporting above.Written with AI from the source above. Every number is checked against the source. Spot a mistake? Corrections.
