skills.rest
richardnguyen0715
richardnguyen0715COMMUNITY
Explore all skills by this author →
GitHub

serving-llms-vllm

Deploy and serve LLMs with vLLM, OpenAI-compatible endpoints, and quantization.

Updated May 11, 2026
Search Tags:#quantization#OpenAI API#high throughput#LLM serving#GPU memory optimization
One-click install
npx skills add https://github.com/richardnguyen0715/keep-it-real --skill serving-llms-vllm-richardnguyen0715
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: serving-llms-vllm
Source: https://github.com/richardnguyen0715/keep-it-real/tree/main/refer-projects/hermes-agent/skills/mlops/inference/vllm
Command: npx skills add https://github.com/richardnguyen0715/keep-it-real --skill serving-llms-vllm-richardnguyen0715

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires vllm, torch, transformers, locust, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of deploying production-grade Large Language Model (LLM) APIs, optimizing inference latency/throughput, and serving models with limited GPU memory.

Core Features & Use Cases

  • High-Throughput Serving: Achieves 24x higher throughput than standard transformers using PagedAttention and continuous batching.
  • OpenAI API Compatibility: Supports OpenAI-compatible endpoints for seamless integration.
  • Quantization: Offers quantization options (GPTQ/AWQ/FP8) for efficient memory usage.
  • Use Case: Deploy this Skill to serve large LLMs like Llama-3 in production environments, optimizing for performance and resource constraints.

Quick Start

Deploy the vLLM serving Skill with the following command:

vllm serve meta-llama/Llama-3-8B-Instruct

Frequently Asked Questions about serving-llms-vllm

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

FAQPage Schema
How do I serve LLMs with high throughput in production?▼

Deploy high-throughput LLM serving in production using vLLM, which utilizes PagedAttention and continuous batching to achieve 24x higher throughput than standard transformers for inference workloads.

How do I optimize GPU memory usage when deploying large language models?▼

Optimize GPU memory usage when deploying large language models by applying quantization techniques like GPTQ, AWQ, or FP8, which significantly reduce memory requirements for serving large models.

Can I use OpenAI API endpoints with my own self-hosted Llama models?▼

Yes, you can use OpenAI-compatible API endpoints with self-hosted Llama models by deploying vLLM, which provides OpenAI-compatible endpoints for seamless integration of models like Llama-3 into existing applications.

What is the best way to deploy a Llama-3 model for inference?▼

The best way to deploy a Llama-3 model for inference is using vLLM with the command `vllm serve meta-llama/Llama-3-8B-Instruct`, which provides high-throughput serving and OpenAI API compatibility.

How do I load test my LLM serving API for production traffic?▼

Load test your LLM serving API for production traffic using Locust, a dependency included in the vLLM serving Skill that helps evaluate inference latency and throughput under simulated concurrent requests.

Does vLLM work with standard transformers for serving large models?▼

vLLM replaces standard transformers for serving large models, achieving 24x higher throughput using PagedAttention and continuous batching rather than relying on the standard transformers pipeline.

Related Skills

Explore More Software Engineering Skills→
zechenzhangAGIzechenzhangAGI
11.5k

model-pruning

Prune large language models using one-shot methods like Wanda and SparseGPT.

Community
Intermediate
SternrasslerSternrassler
1

Load & Performance Testing Skill

Run Go benchmarks and k6 scripts to test API performance under load.

Official
Advanced
ghufronbagaskaraghufronbagaskara

serving-llms-vllm

Deploy vLLM inference servers with OpenAI-compatible API endpoints.

Community
Advanced
PiaoxuemoliPiaoxuemoli

serving-llms-vllm

Serve LLMs with OpenAI-compatible APIs using vLLM and quantization.

Community
Advanced