Benchmark result
prism-ml/Ternary-Bonsai-1.7B-gguf on AMD Radeon Vega Series / Radeon Vega Mobile Series — 38.1 tok/s
Measured with Unsloth Studio on November 13, 2026.
Support llm-bench.io
llm-bench.io is free. If it helped you, I'd appreciate your support to cover the infrastructure costs.
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
1st fastest of 3 runs of this model on AMD Radeon Vega Series / Radeon Vega Mobile Series · median 37.9 tok/s.
sort
- 138.1tok/s26.3Unsloth Studio · Q2_0_g64 · KV f16this run
- 237.9tok/s31.3Unsloth Studio · Q2_0_g64 · KV f16
- 337.4tok/s29.8Unsloth Studio · Q2_0_g64 · KV f16
Reproduce this run
toolUnsloth Studio
modelprism-ml/Ternary-Bonsai-1.7B-gguf (Q2_0_g64)
context8,192
kv cachef16 (assumed default)
think limitnone
samplingtemperature 0.7 · top_p 0.95 · top_k -1 · min_p 0.01 · presence_penalty 0
clientv0.4.99+102
Set those in Unsloth Studio, then:
llm-benchmark benchmark --model "prism-ml/Ternary-Bonsai-1.7B-gguf" --tool "Unsloth Studio"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.