LLM Inference Batching Scheduler

About

This skill provides specialized guidance for designing efficient batching strategies for LLM inference workloads. It helps developers navigate the complex trade-offs between batch count, shape compilation costs, and padding ratios by providing a systematic framework for constraint analysis and parameter optimization. By implementing reusable evaluation infrastructure and rigorous verification strategies, it ensures that inference schedulers meet strict latency and cost budgets while maximizing hardware utilization through intelligent sequence length bucketing.

Key Features

• Systematic parameter search strategies using grid and binary search
• Joint optimization techniques for prompt and generation length bucketing
• Reusable evaluation infrastructure with automatic constraint verification
• Detailed cost function decomposition to identify performance bottlenecks
• Mathematical constraint analysis and hard bound calculation
• 16 GitHub stars

Use Cases

• Reducing shape compilation overhead in systems with fixed unique shape constraints
• Optimizing batch scheduling parameters to stay within strict P99 latency thresholds
• Designing a custom LLM inference engine that needs to minimize padding waste