tanishq-dubey/macos-laguna-s2.1 — explained in plain English
Analysis updated 2026-05-18
Compare compressed versions of the Laguna S 2.1 model to find the best one for your Mac's memory and speed.
Verify that a model variant passes agentic coding tests before using it for development work.
Reproduce benchmark results on your own Apple Silicon Mac to see how the model performs locally.
Review the full results table to trade off memory usage against generation speed for your needs.
| tanishq-dubey/macos-laguna-s2.1 | awslabs/loom | 2dogsandanerd/clawrag | |
|---|---|---|---|
| Stars | 146 | 146 | 147 |
| Language | Python | Python | Python |
| Setup difficulty | moderate | hard | moderate |
| Complexity | 3/5 | 5/5 | 4/5 |
| Audience | developer | ops devops | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires an Apple Silicon Mac with significant unified memory and pinned versions of loader software to avoid runtime bugs.
This repository is a community collection of benchmark scripts for running Poolside's Laguna S 2.1 language model on Apple Silicon Macs. Specifically, it tests different compressed versions, called quantizations, of the model to see how they perform on a M5 Max computer. The work was sponsored by DWS LLC. Quantization shrinks a model so it can run on limited hardware. There are many possible quantizations, and each one trades a bit of accuracy or speed for lower memory usage. The repository's harness measures a range of these versions and records statistics like generation speed, memory usage, and how well the model answers questions or writes code. The goal is to help people pick the best version for their own Mac. The results show that a 2-bit conversion from pipenetwork is the current leader. It passed all 38 assertions in the test suite and reached high speeds on a 128 GB Apple M5 Max. Other conversions from the mlx-community and unsloth groups also passed the agentic coding tests but scored slightly lower overall. The repository keeps a full table of every measured variant, so you can compare options based on your available memory and speed needs. The README is detailed and includes notes on specific runtime issues. For example, some earlier versions had bugs that caused the model to produce broken output. The harness uses specific pinned versions of the loader software to work around these issues. The included chart compares local Apple Silicon measurements against Poolside's published Terminal-Bench results. The full README is longer than what was shown.
A community benchmark collection that tests compressed versions of Poolside's Laguna S 2.1 AI language model on Apple Silicon Macs, measuring speed, memory, and accuracy to help you pick the best version for your hardware.
Mainly Python. The stack also includes Python, MLX.
No license information is provided in the explanation.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Don't trust strangers blindly. Verify against the repo.