Benchmark result
unsloth/gemma-4-31B-it-qat-GGUF on Apple M5 Max — 28.8 tok/s
Measured with llama.cpp b11146-7fe450e19 on October 3, 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
2nd fastest of 2 runs of this model on Apple M5 Max · median 35.4 tok/s.
sort
- 142.1tok/s72.3Unsloth Studio · UD-Q4_K_XL · KV f16
- 228.8tok/s74.6llama.cppthis run
Reproduce this run
toolllama.cpp b11146-7fe450e19
modelunsloth/gemma-4-31B-it-qat-GGUF
context65,536
thinkingon
samplingtemperature 1 · top_p 0.949999988079071 · top_k 64 · min_p 0.05000000074505806 · presence_penalty 0 · repeat_penalty 1
clientv0.4.85+101
Set those in llama.cpp, then:
llm-benchmark benchmark --model "unsloth/gemma-4-31B-it-qat-GGUF" --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.