skills.rest
Piaoxuemoli
PiaoxuemoliCOMMUNITY
Explore all skills by this author →
GitHub

serving-llms-vllm

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

Updated Jun 15, 2026
Search Tags:#quantization#large language models#OpenAI API#high throughput#LLM serving
One-click install
npx skills add https://github.com/Piaoxuemoli/qoobeeHermes --skill serving-llms-vllm-piaoxuemoli
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: serving-llms-vllm
Source: https://github.com/Piaoxuemoli/qoobeeHermes/tree/main/hermes/skills/mlops/inference/vllm
Command: npx skills add https://github.com/Piaoxuemoli/qoobeeHermes --skill serving-llms-vllm-piaoxuemoli

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of deploying production LLM APIs with high throughput and low latency, optimizing inference performance and memory usage.

Core Features & Use Cases

  • High Throughput Inference: 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) to fit large models within limited GPU memory.
  • Use Case: Ideal for deploying large language models in production environments, such as chatbots, assistants, and multi-user applications, where high throughput and low latency are critical.

Quick Start

Install the vLLM package and serve the model with the following command:

pip install vllm
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 large language models with high throughput in production?▼

Serve large language models with high throughput using PagedAttention and continuous batching to optimize inference performance and memory usage for production environments. This approach achieves 24x higher throughput than standard transformers.

Can I use an OpenAI compatible API for my self-hosted LLM?▼

You can use OpenAI compatible API endpoints for self-hosted LLM serving to ensure seamless integration. This allows your existing applications designed for OpenAI to interact directly with your deployed models.

How do I fit large models within limited GPU memory for inference?▼

Fit large models within limited GPU memory for inference by applying quantization techniques like GPTQ, AWQ, or FP8. These options reduce the memory footprint required to load and run large language models.

What do I need to deploy an LLM API with vllm?▼

To deploy an LLM API with vllm, you need the vllm, torch, and transformers libraries installed in your environment. You can then start serving a model using the vllm serve command.

Why is my standard transformers inference slow for multi-user applications?▼

Standard transformers inference is slow for multi-user applications because it lacks PagedAttention and continuous batching. Using vllm optimizes memory usage and enables high throughput, solving low latency bottlenecks.

Does vllm support continuous batching for chatbot deployments?▼

vllm supports continuous batching for chatbot deployments, which is critical for maintaining high throughput and low latency. This allows the server to process multiple concurrent user requests efficiently.

Related Skills

Explore More Software Engineering Skills→
nyxoraAInyxoraAI
5

serving-llms-vllm

Serve OpenAI-compatible LLM APIs with quantization using vLLM and PyTorch/Torch transformers.

Community
Advanced
superfhpsuperfhp

serving-llms-vllm

Serve LLMs with OpenAI-compatible endpoints using vLLM.

Community
Advanced
PlaidmustachePlaidmustache

lambda-labs-gpu-cloud

Launch and manage on-demand Lambda Labs GPU cloud instances via API.

Community
Advanced
lxt070409lxt070409
1

serving-llms-vllm

Automate high-throughput LLM inference with vLLM and OpenAI-compatible endpoints.

Community
Advanced