# Fast Mlx > Optimize MLX code for performance and memory. Use when asked to implement or speed up MLX models or algorithms, reduce latency/throughput bottlenecks, tune lazy evaluation, type promotion, fast ops, compilation, memory use, or profiling. Use when this capability is needed. - Skill: `tomevault-io/fast-mlx` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/fast-mlx` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/fast-mlx/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/fast-mlx --- # Fast MLX ## Workflow - Looks for opportunities to compile functions of mostly elementwise operations. - For models with fixed shape inputs or where the shapes don't change much, compile the entire graph - Replace slow implementations with MLX fast ops - Identify evaluation boundaries and unintended sync points (`mx.eval`, `item()`, NumPy conversions). - Check dtype promotion and scalar usage; keep precision consistent with intent. - Review compilation strategy; avoid unnecessary recompiles and closure captures. - Reduce peak memory via lazy loading order and releasing temporaries before `mx.eval`. - Suggest profiling steps if the bottleneck is unclear. ## References - Read `references/fast-mlx-guide.md` for detailed tips and examples. Use it as the source of truth. ## Output expectations - Provide concrete code changes with brief rationale - Call out changes that need user confirmation (e.g., enabling async eval or shapeless compile). --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/awni) — claim your Tome and manage your conversions.