Model matrix /

d1-omni-600m

Checkpoint d1-omni-600m/hf-02b55d7. Reference: fp32-liquidai/decide-server-d1-torch-cuda-tf5.19.0-torch2.14.1-cu126. Scored on typed-decisions, core tier: 400 cases × 5 questions = 2,000 decisions per run. Δ is accuracy minus the reference's, in points.

Accuracy by variant and machine

Accuracy counts every dispatched decision. The interval (CI) is Wilson 95%; differences smaller than the intervals' overlap are not resolved by 2,000 decisions.

All runs

lower is better for KL, Brier and ECE
Variant · machineAccuracyKL ↓Brier ↓ECE ↓choicenoulscoreDecisions/sInput tok/s*p50 / caseConformance
fp32-liquidai · NVIDIA P100 · 16 GBreference57.4%0.350.1730.06360.5%71.0%44.9%28.58,732355 ms48/49
fp32-liquidai · Apple M4 Pro · 24 GB57.4%0.350.1730.06360.5%71.0%44.9%20.76,332520 ms48/49
fp16-liquidai · NVIDIA P100 · 16 GB57.4%0.350.1730.06260.7%71.0%44.6%29.79,103352 ms48/49
fp16-liquidai · Apple M4 Pro · 24 GB57.4%0.350.1730.06260.5%70.8%44.9%24.57,513432 ms48/49

Decisions/s is over the run's wall-clock time, client to server, so a remote machine includes the network. Engine-only figures are on each run's page. *Input tok/s counts tokens with each model's own tokenizer and prompt layout (d1 counts the state once per question), so it compares one model across machines, not models with each other. p50 is per case (all five questions); runs kept two requests in flight against servers that answer one at a time, so it includes waiting for the other.