Does Qwen3.8 27B UD-Q4_K_XL fit in the AMD Ryzen AI Max+ 395 (Radeon 8060S)'s memory?

Estimate: the UD-Q4_K_XL file is 17.6 GB and needs about 19.0 GB with an ~8K context; the AMD Ryzen AI Max+ 395 (Radeon 8060S) has about 96.0 GB usable, so it fits.

Qwen3.8 27B › AMD Ryzen AI Max+ 395 (Radeon 8060S)

Qwen3.8 27B on the AMD Ryzen AI Max+ 395 (Radeon 8060S)

Qwen3.8 27B runs at about 11.7 tokens/s on the AMD Ryzen AI Max+ 395 (Radeon 8060S) (measured, UD-Q4_K_XL, Vulkan, single-stream llama-server decode).

Best measured: 11.7 tokens/s generation (UD-Q4_K_XL, Vulkan). The source gives no prompt-processing figure.

For comparison, our size-scaling estimate for this combination is 11.8 tok/s.

Every run

Quant	Backend	Generate tok/s	Prompt tok/s	Test	Build / by / notes	Source
UD-Q4_K_XL	Vulkan	11.9	–	decode, llama-server, MTP off	Baseline (spec-off) column of a paired MTP A/B: llama-server single stream (--parallel 1), probe.py median, not llama-bench. Quant per the row's footnote: unsloth UD-Q4_K_XL.	qwen38-mtp community table (GitHub)
UD-Q4_K_XL	Vulkan	11.9	–	decode, llama-server, MTP off	Baseline (spec-off) column of a paired MTP A/B: llama-server single stream (--parallel 1), probe.py median, not llama-bench. Quant per the row's footnote: unsloth UD-Q4_K_XL.	qwen38-mtp community table (GitHub)

tg128 = generating 128 tokens; pp512 = reading a 512-token prompt (llama-bench). LocalScore averages several prompt and output lengths, so its numbers run lower than tg128. "decode, llama-server" = single-stream generation speed measured against a running llama-server (no speculative decoding).

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