Replace slow operators with MLX fast ops to optimize graph performance.
npx skills add https://github.com/WindChimeRan/claude-dotfiles --skill fast-mlx
Please help me install this Agent Skill. Skill: fast-mlx Source: https://github.com/WindChimeRan/claude-dotfiles/tree/main/skills/fast-mlx Command: npx skills add https://github.com/WindChimeRan/claude-dotfiles --skill fast-mlx
MLX models and graphs often suffer from runtime bottlenecks due to non-optimized operators, eager evaluations, and suboptimal memory management. This Skill analyzes MLX graphs to identify opportunities to replace slow ops with fast MLX primitives, improves compilation strategy, and reduces peak memory usage.
Instruct MLX to optimize a given graph by replacing slow operators with fast equivalents and enabling compilation optimizations.
High-intent search queries and answers about installing and using this skill.
Optimize MLX graph performance by substituting slow operations with MLX fast ops and enabling shapeless or shape-aware compilation to reduce runtime bottlenecks during evaluation.
High peak memory usage in MLX models often results from suboptimal memory management, eager evaluations, and unmanaged temporaries. Tuning lazy loading awareness and temporaries minimizes this peak memory.
The best way to locate MLX execution bottlenecks is through performance profiling and debugging guidance, which identifies slow operators and suboptimal compilation strategies within the graph.
Yes, MLX compilation applies across both fixed-shape and lazy-evaluation patterns, allowing you to enable shape-aware or shapeless compilation to optimize workloads effectively.
You should avoid eager evaluation in MLX workloads when optimizing graph performance and minimizing peak memory, as it causes runtime bottlenecks and prevents efficient temporaries management.
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