JacobPEvans/mlx-benchmarks

MLX Benchmarks

Structured benchmark results for local hardware and hosted-provider language model evaluations. Rows include both local-device runs and provider results; recorded host and accelerator fields may be absent for provider rows. Results are published as immutable Parquet shards by the mlx-benchmarks project.

The default configuration opens data/recent/latest.parquet: the canonical shards published within 30 days of the newest one, newest run first. The publish workflow regenerates it from the canonical shards; it adds no rows of its own. Select history to browse every canonical shard, including older hosted-provider evaluations. Optional result and system fields use nulls when a run did not record them. Historical tag columns remain available individually, the tags_json column retains arbitrary tags, and extra_json retains historical columns outside the stable schema.

Original Parquet shards remain in the repository at their original paths. The normalized shards preserve their rows and add null-valued columns where an original shard did not contain a field.

Quickstart

In the dataset SQL console, start with the measurements available for each task and unit:

SELECT
  suite,
  COALESCE(name, metric_name) AS task,
  COALESCE(metric, metric_metric) AS metric,
  COALESCE(unit, metric_unit) AS unit,
  COUNT(COALESCE(value, metric_value)) AS measured_rows
FROM train
WHERE COALESCE(value, metric_value) IS NOT NULL
GROUP BY
  suite,
  COALESCE(name, metric_name),
  COALESCE(metric, metric_metric),
  COALESCE(unit, metric_unit)
ORDER BY suite, task, metric, unit;

Inspect individual throughput runs with their recorded workload:

SELECT
  timestamp,
  COALESCE(name, metric_name) AS task,
  COALESCE(metric, metric_metric) AS metric,
  model, quantization, chip, engine,
  concurrency AS agents, tag_context_tokens_actual AS context_tokens,
  tag_prompt_tokens AS prompt_tokens, tag_max_gen_toks AS output_budget_tokens,
  tag_truncated_rate,
  COALESCE(value, metric_value) AS value,
  COALESCE(unit, metric_unit) AS unit
FROM train
WHERE suite = 'throughput' AND COALESCE(value, metric_value) IS NOT NULL
ORDER BY timestamp DESC
LIMIT 50;

Keep the unit and workload fields with each result when comparing rows. A null context, prompt, output budget, or completion field means that detail was not recorded for that run; it is not a zero or a value shared with other runs.

Result fields

The normalized default split uses the flat result columns name, metric, value, and unit. In original legacy shards, the equivalent nested result fields may appear as metric_name, metric_metric, metric_value, and metric_unit. When combining downloaded current and legacy shards, coalesce each pair and keep the corresponding unit:

COALESCE(name, metric_name) AS name,
COALESCE(metric, metric_metric) AS metric,
COALESCE(value, metric_value) AS value,
COALESCE(unit, metric_unit) AS unit

Optional system and workload columns include machine label, chip, gpu, engine, quantization, concurrency, and projected tag_* fields such as tag_context_tokens_actual, tag_prompt_tokens, and tag_max_gen_toks. These fields are null when the run did not record or use that dimension. tags_json retains arbitrary tags, and extra_json retains historical fields outside the stable schema.

Detailed campaign envelopes add stable campaign_<group>_<field> columns for hardware, software, model, run, speed, resource, quality, provenance, and cost dimensions. campaign_dimension_null_reasons_json retains the reason codes for explicit unknown or inapplicable values. Historical shards receive nulls in these columns during schema normalization.

Canonical rows use hardware labels for machine identity. Original shards remain at their original paths as historical source data.