Datasets:

JacobPEvans
/

mlx-benchmarks

pretty_name: MLX Benchmarks
license: apache-2.0
language:
  - en
tags:
  - benchmark
  - evaluation
  - llm
  - mlx
  - apple-silicon
  - throughput
  - latency
  - code-generation
  - reasoning
  - math
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*.parquet

MLX Benchmarks

Structured benchmark results for MLX-quantized and other locally-hosted LLMs on Apple Silicon. Covers throughput, time-to-first-token, tool-calling, code generation, reasoning, knowledge, and math suites.

Results are produced by a sweep harness that wires upstream evaluation tools against a local vllm-mlx inference server:

EleutherAI/lm-evaluation-harness — coding, reasoning, knowledge, math
linusvwe/MLXBench — throughput and time-to-first-token
vllm benchmark_serving — performance second opinion
huggingface/lighteval — broader task coverage

All data here is generated on Apple Silicon hardware (MINISFORUM MS-A2 / M4 Max class), stored in flat columnar Parquet for easy querying, and appended to via unique-filename commits so historical shards are never overwritten.

Quickstart

from datasets import load_dataset

ds = load_dataset("JacobPEvans/mlx-benchmarks")
print(ds)

# Example: average throughput per model
import pandas as pd
df = ds["train"].to_pandas()
throughput_rows = df[df.suite == "throughput"]
print(
    throughput_rows.groupby("model")["metric_value"]
    .mean()
    .sort_values(ascending=False)
)

Raw Parquet fetch (token-optimal for agents):

curl -sSL \
  https://huggingface.co/datasets/JacobPEvans/mlx-benchmarks/resolve/main/data/train-00000-of-00001.parquet \
  -o run.parquet

Schema

Each input benchmark run produces a JSON envelope (see schema.json in this repo for the authoritative v1 spec). The envelope is exploded row-wise into flat scalar columns — one row per entry in the envelope's results[] array. Skipped runs become a single sentinel row with null metric columns and skipped=true. This mirrors the columnar layout used by the Open LLM Leaderboard contents dataset.

Column	Type	Notes
`suite`	string	One of: throughput, ttft, tool-calling, code-accuracy, framework-eval, capability-comparison, coding, reasoning, knowledge, evalplus, math-hard
`model`	string	Full model identifier
`git_sha`	string	Commit SHA of the generator at run time
`timestamp`	string	ISO-8601 UTC start of the run
`trigger`	string	`schedule`, `pr`, `workflow_dispatch`, or `local`
`schema_version`	string	Envelope schema version (currently `"1"`)
`pr_number`	int64	PR number if triggered by a pull request, else null
`skipped`	bool	True for sentinel rows where the suite was skipped
`os`	string	Operating system at run time
`chip`	string	CPU/chip identifier
`memory_gb`	int64	Total system RAM
`vllm_mlx_version`	string	Backend version if captured
`runner`	string	Runner label or `local`
`metric_name`	string	Individual test/measurement name
`metric_metric`	string	Metric family (e.g. `throughput`, `latency`, `score`)
`metric_value`	float64	Numeric value
`metric_unit`	string	Unit (`tok/s`, `seconds`, `ratio`, ...)
`tags_json`	string	JSON-serialized tag dict (per-suite custom metadata)
`errors_json`	string	JSON-serialized list of non-fatal errors from the run

Nested fields from the envelope (tags, errors) are preserved as JSON-serialized strings so no information is lost — rehydrate with json.loads(row["tags_json"]).

Update cadence

New rows are appended on every sweep via a unique-filename commit pattern (data/run-{timestamp}-{sha}-{suite}-{model}.parquet). Historical shards are never overwritten. load_dataset() concatenates all data/*.parquet files into a single train split at load time.

License

Apache 2.0 — same as the underlying upstream evaluation tools.