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inference-optimization

Optimize LLM inference serving to reduce latency, cost, and scalability constraints.

1|Updated Jun 4, 2026
Search Tags:#LLM#performance optimization#cost reduction#inference#hardware usage
One-click install
npx skills add https://github.com/hung-phan/ml-skills --skill inference-optimization-hung-phan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: inference-optimization
Source: https://github.com/hung-phan/ml-skills/tree/main/skills/ml-review/references/ml-training/inference-optimization
Command: npx skills add https://github.com/hung-phan/ml-skills --skill inference-optimization-hung-phan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenges of serving Large Language Models (LLMs) at scale, focusing on latency, cost, and resource utilization.

Core Features & Use Cases

  • Latency Reduction: Techniques to decrease the time between receiving a request and generating a response, such as speculative decoding and continuous batching.
  • Cost Optimization: Strategies to reduce the cost of serving LLMs, including model quantization, efficient hardware usage, and choosing the right inference engine.
  • Scalability: Ensuring the system can handle increased load without degradation in performance.

Quick Start

To get started, analyze the inference performance of your LLM and implement optimizations based on the metrics and techniques described in the skill documentation.

Frequently Asked Questions about inference-optimization

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

FAQPage Schema
How do I reduce latency when serving large language models?▼

To reduce latency when serving large language models, you can apply optimization techniques like speculative decoding and continuous batching to decrease the time between receiving a request and generating a response.

What is the best way to optimize LLM inference for cost?▼

The best way to optimize LLM inference for cost is implementing model quantization, ensuring efficient hardware usage, and choosing the right inference engine to reduce overall serving expenses.

What are continuous batching and speculative decoding in LLM inference?▼

Continuous batching and speculative decoding are optimization techniques for LLM inference that enhance latency by minimizing idle processing time and predicting token generation steps.

How do I ensure LLM scalability without degrading performance?▼

To ensure LLM scalability without performance degradation, you must implement optimization strategies that allow your serving system to handle increased request loads efficiently.

Does model quantization affect hardware usage for LLM inference?▼

Model quantization directly affects hardware usage for LLM inference by reducing the memory footprint and computational requirements, which helps decrease serving costs and improve resource utilization.

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