pbhappliedsystems/quant_eval_efficiency_and_footprint

quant_eval — Efficiency and footprint

One row per published run: stored weight artifact bytes before and after quantization, compression ratio, observed evaluation wall-time ratio with an explicit direction label, and token throughput.

Part of the quant_eval public corpus: a per-case behavioral evaluation of full-weight and quantized large language models across eight agent-relevant task families, with paired statistical testing.

Cite this dataset: 10.5281/zenodo.22010723 — concept DOI, always resolves to the latest version. This exact deposit: 10.5281/zenodo.22010724 — version DOI, frozen. Cite this one where reported numbers must stay verifiable against the object referenced.

What this file contains

File	Rows	Columns
quant_eval_efficiency_and_footprint.csv	6	22

Supporting files: source_bundle_checksums.json.

Corpus scope

Run	Model	Baseline	Quantized	Substrate	Licence
Mistral_Nemo_Instruct_2407_20260814_030505	mistralai/Mistral-Nemo-Instruct-2407	gguf_f16	gguf_q4_k_m	local	Apache-2.0
Mistral_Nemo_Instruct_2407_20260815_113254	mistralai/Mistral-Nemo-Instruct-2407	gguf_f16	gguf_q5_k_m	local	Apache-2.0
Mistral_Nemo_Instruct_2407_20260816_084553	mistralai/Mistral-Nemo-Instruct-2407	gguf_f16	gguf_q8_0	local	Apache-2.0
Qwen2.5_14B_Instruct_1M_20260815_220633	Qwen/Qwen2.5-14B-Instruct-1M	modal_f16	modal_q4_k_m	Modal	Apache-2.0
Qwen2.5_32B_Instruct_20260815_081051	Qwen/Qwen2.5-32B-Instruct	modal_f16	modal_q4_k_m	Modal	Apache-2.0
Qwen2.5_7B_Instruct_20260814_234822	Qwen/Qwen2.5-7B-Instruct	gguf_f16	gguf_q4_k_m	local	Apache-2.0

Every run evaluates a full-weight baseline and a quantized variant of the same model against the identical locked fixture set, case for case. Statistical comparison is paired: the two-sided exact McNemar test on per-case outcomes, with Wilson intervals on the rates.

Figures

Observed evaluation wall-time ratio (full weight divided by quantized) for all six published pairs. Hatched bars fall below parity — the quantized run was slower. Observed harness wall time on the recorded hardware and backends; not a controlled throughput benchmark and not a general claim about quantization performance at any precision on any hardware.

Figures are generated directly from the harness rollups by the published build tooling; no plotted value is recomputed, smoothed, or fitted.

Columns

quant_eval_efficiency_and_footprint.csv

  run_id                                        model_id                                      baseline_kind
  quant_type                                    baseline_bytes                                quantized_bytes
  compression_ratio                             size_reduction_fraction                       measurement_scope
  excludes                                      baseline_runner                               quantized_runner
  execution_substrate                           baseline_tokens_per_second                    quantized_tokens_per_second
  observed_wall_time_ratio                      wall_time_direction                           throughput_ratio
  token_volume_cost_proxy_ratio                 monetary_cost_ratio_status                    artifact_footprint_status
  hardware_backend_scope

Verification

This corpus is derived from sanitized publication bundles produced by the quant_eval harness. It is designed to be checked rather than trusted:

• source_bundle_checksums.json, included here, republishes, verbatim, the SHA-256 digest and byte length of every file in every source bundle. No source file was modified.
• Before this file was written, the builder verified all 72 source-file digests and independently recomputed all 96 family x runner pass rates from the raw per-case rows, matching the harness rollups exactly.
• The figures here are not derived from the per-case results dataset. Stored-artifact byte counts and observed wall time are recorded by the harness at run time and are carried through from the source bundles unchanged; they are traceable through the bundle digests above, not recomputable from per-case rows. Pass-rate aggregates, which are recomputable, live in the paired degradation statistics (D5) and family pass rates (D6) datasets.

Limits you should know before using this

• Decoding conditions are not uniform across models. Temperature follows each publisher's own model card, so cross-model comparison of absolute pass rates is confounded. Within-run pairing is unaffected, which is what the paired test requires. The conditions are published per row and per run so they can be filtered on.
• Runs on the Modal substrate record seed status unsupported — the deployed method signature accepts no seed — so those runs are not exactly reproducible. Local runs applied a fixed seed.
• Runtime figures are observed harness wall time on the recorded hardware and backends. They are not a controlled throughput benchmark and not a general claim about quantization performance at any precision on any hardware. Direction is published as an explicit label because not every measured pair is a speedup.
• The fuzz family is an adaptive trajectory evaluated from identical starting fixtures. Its paired test compares complete case outcomes, not identical post-divergence prompts.
• Calibration runs are not published. Runs that informed a published run are disclosed by identifier in calibration_lineage.csv, published in the run provenance dataset (D4), so the record is complete without releasing provisional numbers.

Citation

@dataset{pbh_quant_eval_d7,
  author    = {Hill, Patrick},
  title     = {quant_eval Efficiency and footprint},
  publisher = {PBH Applied Systems, LLC},
  year      = {2026},
  doi       = {10.5281/zenodo.22010723},
  note      = {Version DOI: 10.5281/zenodo.22010724},
  license   = {CC-BY-4.0}
}

Licence

Creative Commons Attribution 4.0 International (CC BY 4.0). See LICENSE. Commercial use is permitted; attribution is required.

This corpus describes third-party models and redistributes no model weights. Each evaluated model remains under its own licence, recorded per run in the run provenance dataset.

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Produced by build_datasets.py 2.4.0 from quant_eval publication bundles. Built 2026-08-19.