Benchmark result

Qwen3.8-27B-UD-IQ4_XS on 2× NVIDIA GeForce RTX 5060 — 38.1 tok/s

Measured with llama.cpp b10883-91f6a6cf3 on September 9, 2026.

What the model built

The coding scenario asks for a playable game in a single HTML file. This is exactly what the model returned, unedited; the frame only adds a line that tells this page how tall it is.

How this run compares

1st fastest of 2 runs of this model on 2× NVIDIA GeForce RTX 5060 · Multi-GPU · median 31.6 tok/s.

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138.1tok/s79.0llama.cppthis run
225.1tok/s84.8llama.cpp

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Qwen3.8-27B-UD-IQ4_XS on other hardware

Reproduce this run

toolllama.cpp b10883-91f6a6cf3

modelQwen3.8-27B-UD-IQ4_XS

context65,536

thinkingon

effortxhigh (model default)

samplingtemperature 0.6000000238418579 · top_p 0.8999999761581421 · top_k 0 · min_p 0.05000000074505806 · presence_penalty 0 · repeat_penalty 1

clientv0.4.58+97

Set those in llama.cpp, then:

llm-benchmark benchmark --model "Qwen3.8-27B-UD-IQ4_XS" --tool "llama.cpp"

The client prompts for context length and thinking mode, and for the KV cache dtype on Unsloth Studio. Sampling is left at the model default — the values above are what the tool reported using, not overrides the client sent.