MLX vs GGUF on Apple Silicon: Benchmarking the Same Local Model Two Ways

Description and use

MLX and GGUF deliver comparable build times and app functionality, but MLX offers lower variance and never hits the 60‑minute cap. GGUF is slightly faster on compute‑heavy tasks but more spiky.

Problem

Choosing between GGUF and MLX model formats for local coding agents on Apple Silicon, balancing speed, consistency, and quality.

Outcome

MLX ties GGUF on overall build time and app‑work‑ability, with lower run‑to‑run variance and no 60‑minute caps. GGUF is marginally faster on compute‑heavy tasks but shows higher peak times and occasional caps due to harness bugs.

Workflow

• 1. Prepare identical model weights in GGUF and MLX formats.
• 2. Set up a three‑node Apple Silicon fleet with LM Studio serving the model.
• 3. Run swarm‑gym in benchmark mode with the medium tier (5 runs × 3 archetypes).
• 4. Collect structured event logs, per‑task metrics, and golden‑value checks.
• 5. Analyze build times, variance, and app‑work‑ability metrics.
• 6. Identify and fix harness bugs (e.g., integrate‑verify step cap).

Evidence

MLX had lower run‑to‑run variance (stdev 580s vs 751s), a lower p90 (3050s vs 3601s), and never hit the 60‑minute per‑run cap.

Source

Both builds achieved ~87% app‑works (13/15) when judged by running, despite raw checks favoring MLX.

Source