Reproduction
This protocol separates reproduction into two acceptance levels. Offline evidence verification runs on any machine. A host matching the fixed hardware and software controls can continue with a performance replay that emits a new environment receipt. Both levels use the same model, prompt, ACL, and output identities.
The current untouched-Q6_K capture records a clean source revision and exact binary identities. A replay from a newer revision is still a new experiment and must retain its own Git revision, binary hashes, and environment receipt. Older mixed-artifact captures that disclose a dirty worktree remain historical evidence.
Acceptance baseline
1. Acquire the source and freeze experiment identity
Run in Windows PowerShell:
The current pure-Q6_K gate records clean source revision eb6aeda59561eff3e4e7592704cab6fc863b72c7. Replaying from a newer clean revision is valid, but it is a new experiment and must not overwrite the checked-in JSON.
2. Run the model-free offline verifier first
This command does not load the 22.88 GB model and does not require an NVIDIA GPU. It verifies 14 file SHA-256 values and recomputes sample counts, medians, minima, quality scores, request-wide throughput, acceptance, replay counts, and deterministic output identity:
A passing process exits with code 0 and includes:
Any changed evidence byte or statistic produces a nonzero exit code and names the mismatched field.
3. Match the acceptance host
The 176.61 token/s value is a boundary on this host, not a cross-hardware promise:
The same protocol can run after changing the GPU, driver, clock, display load, or CPU topology, but label that result as a new platform. Do not copy an inapplicable affinity mask merely to pass the gate.
4. Build the pinned CUDA profile
The patch tool must confirm that the binding and embedded llama.cpp patches are applied. The runner rejects any backend other than exclusive llama.cpp.
5. Verify the model and inputs
Register the untouched Q6_K artifact as qwen3.8-27b-q6-k, then select its Power data directory. These three repository inputs must match exactly:
6. Replay the prefix-FR8192 peak end to end
Close GPU-consuming applications and run in a terminal allowed to lock the GPU clock:
The runner restores the GPU clock in finally and retains failed reports, so environment contention can be distinguished from a real performance regression.
7. Accept the new result
Retain these files under $benchmarkRoot:
pure-q6-fr8192-k7s6-replay.json: nine raw samples, statistics, and output digest;pure-q6-fr8192-k7s6-replay.environment.json: Git, binary, model, ACL, prompt, GPU, process, and power identities;- matching stdout and stderr logs: backend initialization and failure diagnosis.
The replay passes only when all nine requests generate 1,024 tokens, the steady median is at least 175 token/s, every output SHA-256 is identical, model identity matches exactly, the backend is exclusive, the worktree is clean, and the requested host controls actually took effect.
The complete Windows/CUDA guide also covers the paired full-vocabulary control, the 12-task pure-Q6_K calibration, and the previous mixed-artifact gates. The quality-matrix protocol explains how to rerun the existing 100-task × 3-run evaluation.
