nickh007/kv-reuse-econ-traces

KV Reuse Econ Traces — a headline and the closed form that predicts it, side by side

65 per-workload first-touch prefill accounting rows: 36 from a synthetic size ramp, 29 from the real Mooncake FAST'25 trace.

Why this dataset exists

We published a 90.0% mean first-touch prefill cut. It is an exact arithmetic identity, not a measured efficiency — and rather than say so in a footnote, this dataset ships the closed form as a column beside the measurement:

first_touch_cut == closed_form_cut       # true on 36 of 36 synthetic rows
closed_form_cut  = 256 * (n // 256) / n  # chunk size × prompt length. That is all.

The value is fixed by two numbers we chose — the LMCache chunk size and the prompt-length ramp. On the eight largest blocks it becomes 0.947 on identical hardware. The pre-registered 0.30 floor was arithmetically unfalsifiable: any prompt ≥ 512 tokens clears it by construction.

The real finding in that run is `zero_tenant_leak`, true on every row. That is a correctness result. The 90% is accounting.

We ship the refutation as a column because a caveat in prose gets dropped when the number is quoted.

Schema

The Mooncake rows carry closed_form_cut = null: real prompts have no chosen ramp, which is why their mean cut (0.668) is lower and 6 of 29 fall below the 0.30 floor. Those six are in the data.

Usage

from loader import load

rows = load("kv-reuse-econ-traces", where={"source": "synthetic_ramp"})
assert all(r["first_touch_cut"] == r["closed_form_cut"] for r in rows)   # 36/36

real = load("kv-reuse-econ-traces", where={"source": "mooncake_fast25_trace"})
below = [r for r in real if r["first_touch_cut"] < 0.30]                 # 6 of 29

Provenance

Third-party data: the 29 mooncake_fast25_trace rows are derived from the Mooncake trace — Apache-2.0, © kvcache-ai, <https://github.com/kvcache-ai/Mooncake> (USENIX FAST '25). They are per-workload accounting aggregates and carry no prompt or response text. See `NOTICE`. Attribution travels with the rows if you redistribute them.

Regenerate: python oss/datasets/export_datasets.py; verify with --check.

Honest limits

• —No latency, no FLOPs, no throughput was measured. These are token-accounting quantities.
• —The store-fetch cost is not netted out. Fetching KV from a remote store is not free and does not appear here.
• —The synthetic headline is ramp-dependent, and the dataset lets you demonstrate that in one line.
• —`zero_tenant_leak` is an observation over these workloads, not a proof of isolation.

The commercial edition

The pooling fabric, the cross-instance reuse path, and the fleet controller that these traces characterise are covered by filed claims and licensed separately. The traces, the benchmark, and the refutation of our own headline are free.

Licence

Apache-2.0. Mooncake-derived rows: Apache-2.0, © kvcache-ai — see `NOTICE`.

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The rest of the portfolio

25 artifacts, one idea: a measurement you cannot check is a press release. Every tool here reports; none of them gates.

Tools

Benchmarks — each recomputes one of our own published numbers from its certificate

Datasets

Try it in a browser — no install, no GPU

Documentation

Everything above, explained in one place: <https://nickharris808.github.io/evidence-docs/> — the tutorial, what this proves and what it does not, and a CLI reference generated by running --help on every published command.

The commercial edition

Everything above is measure-only and Apache-2.0: it tells you what is true and never acts on it. The enforcement side — binding a partition key at the admission decision, the compiled gate corpus, and the certificate-issuing faucet — is covered by filed patents and licensed separately.

Reading is free. Enforcing is licensed. <!-- /PORTFOLIO -->