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llm-inference-batching-scheduler

Design LLM inference batching schedulers balancing compilation cost and latency constraints.

127|27|Updated Mar 27, 2026
Search Tags:#optimization#latency#batching#inference#shapes#bucketing#cost-model
One-click install
npx skills add https://github.com/lazyFrogLOL/Harness_Engineering --skill llm-inference-batching-scheduler-lazyfroglol
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-inference-batching-scheduler
Source: https://github.com/lazyFrogLOL/Harness_Engineering/tree/main/skills/llm-inference-batching-scheduler
Command: npx skills add https://github.com/lazyFrogLOL/Harness_Engineering --skill llm-inference-batching-scheduler-lazyfroglol

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides a structured methodology for designing batching schedulers that optimize LLM inference workloads on compilation-based accelerators, balancing compilation cost, padding waste, and latency targets.

Core Features & Use Cases

  • Phase-driven analysis: perform mathematical cost modelling, analyze request distributions, and derive constraints before coding.
  • Parameterized search and invariant checks: implement reusable validation that ensures coverage, shape validity, and performance trade-offs.
  • End-to-end workflow: from plan generation to verification, with clear guardrails and structured experimentation to meet latency and cost goals.

Quick Start

Design a batching scheduler plan that minimizes compilation cost while meeting P95 latency targets for a given LLM inference workload.

Frequently Asked Questions about llm-inference-batching-scheduler

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize LLM inference batching to reduce padding waste and meet latency constraints?▼

Optimize LLM inference batching by applying a cost-model-based planner to balance shape compilation costs, padding waste, and P95/P99 latency targets for compilation-based accelerators receiving mixed-length prompts.

What is bucketing in LLM inference scheduling and when do I need it?▼

Bucketing in LLM inference scheduling groups mixed-length prompts into valid batch shapes to minimize padding waste and compilation cost, needed when handling diverse generation requests on compilation-based accelerators.

How to design a batching scheduler that satisfies P95 latency targets for mixed-length prompts?▼

Design a batching scheduler using phase-driven mathematical cost modelling and parameterized search to derive constraints, ensuring request coverage and valid batch shapes while meeting P95 latency targets.

Does this batching approach work for compilation-based accelerators handling dynamic request distributions?▼

Yes, this batching approach works for compilation-based accelerators by analyzing request distributions and applying invariant checks to ensure full coverage of mixed-length generation requests with valid batch shapes.

What are the limitations of cost-aware batching schedulers for LLM inference workloads?▼

Cost-aware batching schedulers require balancing trade-offs between shape compilation costs, padding waste, and latency constraints, needing structured experimentation and invariant checks to ensure valid batch shapes and full request coverage.

Can I use parameterized search to validate batch shapes and ensure full coverage of inference requests?▼

Yes, use parameterized search with reusable validation and invariant checks to ensure full coverage of inference requests and valid batch shapes while managing performance trade-offs and compilation costs.

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