Benchmark result
Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp-ASCII-Condensed on NVIDIA GeForce RTX 5080 — 58.4 tok/s
Measured with llama.cpp manual on October 10, 2026.
What the model built
Open full screen →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
Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp-ASCII-Condensed on other hardware
Reproduce this run
toolllama.cpp manual
modelQwen3.8-27B-GSQ-RCO-IQ3_S-mtp-ASCII-Condensed
context65,536
thinkingon
effortmedium
think limitnone
samplingtemperature 1 · top_p 0.949999988079071 · top_k 20 · min_p 0 · presence_penalty 0 · repeat_penalty 1
clientv0.4.89+102
Set those in llama.cpp, then:
llm-benchmark benchmark --model "Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp-ASCII-Condensed" --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.