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Running Q3 on my AMD RX 9070XT. 32k context and 32/TPS. Apart from the context window preventing it from doing any large tasks, this thing is seriously powerful. I could probably push it to 64k context. Local open models are the future, and I am definitely getting a more powerful card. Very fun!
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9070XT operator here: I'm using llama.cpp with the same model and quant and I'm getting 87,000 for my context limit. I tried the Unsloth models but they lowered it to around 30-40K so I went back to upstream.

I'm on Linux and using some sort of unholy mess of ROCM libraries that I don't understand.

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Could you comment more on how you set this up? I have a mostly idle 9070XT I use for gaming, and I was considering using it with the newer local open models. Many thanks.
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I got Qwen3.8-27B running on my RX 9070 XT by setting up AMD’s ROCm 7.14/TheRock runtime for gfx1201 and adding the required HIP/LLVM paths to Windows. The official llama.cpp ROCm build detected the card correctly, but kept crashing with 0xC0000005 while loading the model, so I switched to the Windows Vulkan build of llama.cpp instead, which worked reliably.

I’m running Qwen3.8-27B-Unleashed UD-Q3_K_XL, which is a ~12.3 GiB Q3 quant, fully offloaded to the 16 GB 9070 XT. I disabled the vision projector to save VRAM and use one inference slot, Flash Attention, Q4 KV cache, --fit off, and --ctx-checkpoints 0. I’m running it with a 64K context window. The AMD driver also needs to be recent enough for ROCm 7.14; I targeted Adrenalin 26.6.4 or newer.

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What are you offloading to ram (or even CPU)? I’m using a 9080 (not XT) and having trouble with context/token rates
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I’m running Qwen3.8-27B-Unleashed UD-Q3_K_XL, which is a ~12.3 GiB Q3 quant, fully offloaded to the 16 GB 9070 XT. I disabled the vision projector to save VRAM and use one inference slot, Flash Attention, Q4 KV cache, --fit off, and --ctx-checkpoints 0. I’m running it with a 64K context window. The AMD driver also needs to be recent enough for ROCm 7.14; I targeted Adrenalin 26.6.4 or newer.
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You can offload the vision projector to CPU/sysRAM
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Awesome. How would you setup that ?
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Running Q3 on 5060ti with 64k context. It runs great
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There is an interesting new dynamic 3 bit quantisation I have been meaning to test:

https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF

Luke of Luke’s Dev Lab on YouTube had a look at it. It seems to outperform the typical 3-bit quantisation but whether it outperforms the new Unsloth dynamic I don’t know.

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Q3 XL and Q3 XS are the two I'm trying to decide on
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You might want to test this new dynamic GGUF:

https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF

(I don’t know much about it, just saw a YouTube video about it last night)

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Another one to try:

https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-GGUF

Runs the 3bit model faster than the 2bit one runs on my old-ass card. Can’t vouch for its intelligence yet, but i suspect whatever loss in smarts it takes is made up for by the extra resolution.

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I have a 5080, three OpenAI Pro token resets, and I’m on paternity leave. Astra seems pretty clever. Maybe I’ll give it a task.
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