A reproducible local benchmark harness for comparing Laguna S 2.1 MLX quantizations on Apple Silicon, recording token throughput, memory, and task scores across generation and agentic coding tasks.

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MIT

Last updated

2026-07-22

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FR-AI / ANALYSIS

Why it is worth attention

Offers a thorough, reproducible comparison of many quantized Laguna S 2.1 variants on Apple Silicon, with detailed metrics including token throughput, peak MLX memory, and task scores, plus long-context retrieval tests and a curated quant ladder for different memory budgets.

Who it is for

  • MLX users on Apple Silicon evaluating local LLM performance
  • Developers selecting the best quantization trade-off between speed and quality for coding tasks
  • Researchers benchmarking model efficiency across different Apple Silicon configurations
  • macOS power users running local LLM inference with constrained memory

Use cases

  • Comparing quantization methods (e.g., 2-bit vs 4-bit) for the same model on a given Mac
  • Identifying the fastest usable quantization that passes all quality assertions for coding workloads
  • Reproducing benchmark results on a new Apple Silicon machine to inform deployment decisions
  • Adding community-contributed results from other Macs into a unified CSV for broader comparison

Strengths

  • Comprehensive metrics: token throughput, prefill speed, peak MLX memory, load time, and task scores for both generation and agentic coding tasks
  • Reproducible: pinned model revisions, deterministic decoding settings, and a locked environment via uv
  • Supports multiple engines (MLX-LM, mlx-vlm, OpenAI-compatible server) and provides a curated quant ladder for different RAM budgets
  • Open to community contributions with automated CSV merging and clear contribution guidelines

Considerations

  • Only runs on Apple Silicon (M-series) hardware due to MLX dependency
  • Some quantization variants require bundled custom loaders (e.g., pipenetwork's MLX-LM loader) rather than stock mlx-vlm
  • Results are machine-specific and may change with macOS updates, Metal kernel versions, or runtime revisions

README quick start

Laguna S 2.1 MLX benchmark

This repository contains a reproducible local harness for comparing Laguna S 2.1 MLX quantizations on Apple Silicon. Each run records the raw output and task score along with token and prefill throughput, peak MLX memory, load time, package versions, model revision, and machine metadata.

This work was sponsored by DWS LLC.

The new leader on the M5 Max is pipenetwork/Laguna-S-2.1-MLX-2bit through MLX-LM. It passed all 38 assertions, averaged 63.86 generation tok/s across the three generation tasks, and reached 68.49 tok/s in the fixed 256-token decode. It needs the conversion's bundled Laguna loader, so the harness loads that file from a pinned revision without modifying the installed MLX-LM package. The measurements are in BENCHMARK_RESULTS.md.

Results at a glance

These results were measured on a 128 GB Apple M5 Max using macOS 27.0, Python 3.13.12, MLX 0.32.0, mlx-vlm 0.6.6, and MLX-LM 0.31.3. The score combines three generation tasks and three agentic coding tasks. Throughput is weighted across the generation tasks.

The left panel reproduces the Terminal-Bench 2.1 figures in Poolside's model card. The right panel comes from this repository's committed CSV and selects the frontier plus one useful representative from each quant/runtime family; the table and CSV below contain every measured variant. The panels use separate scales and are not the same evaluation. Regenerate the SVG with:

uv run --frozen laguna-bench chart

Rows are sorted by overall score, then weighted generation throughput.

QuantOverall scoreGenerationAgenticGeneration tok/sPeak GBSuite time
pipenetwork/Laguna-S-2.1-MLX-2bit1.0001.0001.00063.8639.3187.47s
mlx-community/Laguna-S-2.1-oQ2e1.0001.0001.00040.8537.7787.38s
unsloth/Laguna-S-2.1-GGUF UD-Q2_K_XL0.8750.7501.00060.3737.99 RSS40.17s
pipenetwork/Laguna-S-2.1-MLX-3bit0.8750.7501.00058.6352.8865.82s
unsloth/Laguna-S-2.1-GGUF UD-Q3_K_M0.8750.7501.00051.2751.34 RSS65.19s
unsloth/Laguna-S-2.1-GGUF UD-IQ4_XS0.8750.7501.00051.0454.64 RSS54.23s
unsloth/Laguna-S-2.1-GGUF UD-IQ4_NL0.8750.750

Description

Community benchmarks and scripts for running Poolside Laguna S 2.1

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