Bonsai Demo explained: local inference with a fork-specific model
Measure Bonsai without repeating upstream speed and quality claims
Design a test that can reveal incompatibility before timing tokens.
What you will learn
- Verify correctness first
- Control measurement conditions
- Keep upstream figures attributed
Before you start
- A target machine with measured memory and disk
- Permission to inspect downloaded model and binary files
Capture the exact artifacts and one correctness failure before claiming a useful deployment.
Key takeaways
- Compatibility precedes speed.
- Benchmarks depend on exact hardware and prompt conditions.
- No performance result was produced for this series.
Verify correctness first
Run a fixed set of text prompts that includes a straightforward answer, a refusal or uncertainty case and a long-context probe within your hardware budget. Save raw outputs and model revision.
If responses are corrupted, a speed number is meaningless. The format document warns that one development Q2_0 path may appear to load on stock llama.cpp while omitting required transforms.
Control measurement conditions
Record CPU or GPU, driver, backend build, format, context length, prompt length, sampling settings, warmup and tokens generated. Measure prefill and generation separately when the runtime exposes them.
The community-benchmark folder contains submissions under varied hardware and templates. Compare only rows with compatible model, settings and workload; otherwise treat them as examples of what to measure.
Keep upstream figures attributed
The README presents figures for retained quality, model size and context length. This series did not reproduce those tests. In a report, link each upstream number to its stated methodology and keep local measurements in a separate column.
A good result includes failures and memory peaks, not just the fastest successful run. Unknown values stay blank until a test produces them.
Decision guide
| Criterion | Option A | Option B |
|---|---|---|
| Best when | You need predictable behavior and easy auditing | You need adaptive optimization and have reliable telemetry |
| Main risk | May leave performance on the table | Can become difficult to explain or debug |
Implementation steps
- 1
Run a correctness fixture before benchmarking.
- 2
Record hardware, backend, model and settings.
- 3
Separate upstream claims from local timings and failures.
Copy-ready example
model_file,backend,device,context,prompt_tokens,output_tokens,prefill_s,generation_s,correctness
PQ2_0,,,,,,,,not-runFrequently asked questions
Did EasyAI measure Bonsai tokens per second?
No. The article gives an evaluation plan only.
Can I compare all community rows directly?
Not without matching their model files, hardware, backend and workload.
Sources
- Bonsai Demo / README.mdSource checked 2026-09-29
- Bonsai Demo / BACKEND-SUPPORT.mdSource checked 2026-09-29
- Bonsai Demo / community-benchmarks/bonsai2/README.mdSource checked 2026-09-29