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serving-llms-vllm

Serve large language models with vLLM's PagedAttention and continuous batching.

Updated Apr 29, 2026
Search Tags:#LLM#OpenAI API#inference serving#PagedAttention#Continuous Batching#High Throughput#Quantization#Tensor Parallelism#Production
One-click install
npx skills add https://github.com/fikriaf/agentos --skill serving-llms-vllm-fikriaf
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: serving-llms-vllm
Source: https://github.com/fikriaf/agentos/tree/main/src/agentos/skills/mlops/inference/vllm
Command: npx skills add https://github.com/fikriaf/agentos --skill serving-llms-vllm-fikriaf

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 serving Large Language Models (LLMs) with high throughput, optimizing inference latency, and serving models that have limited GPU memory.

Core Features & Use Cases

  • High Throughput: Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching.
  • Optimized Latency: Optimizes inference latency for production LLM APIs.
  • Limited GPU Memory: Serves models with limited GPU memory by using techniques like quantization and tensor parallelism.
  • OpenAI Compatibility: Supports OpenAI-compatible endpoints.
  • Quantization: Offers quantization options (GPTQ/AWQ/FP8) for further optimization.
  • Tensor Parallelism: Enables tensor parallelism for larger models.
  • Use Case: Deploy production LLM APIs, optimize inference latency/throughput, or serve models with limited GPU memory.

Quick Start

Use the vLLM skill to serve the Llama-3-8B-Instruct model at high throughput. Run 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 and low inference latency?▼

Serve LLMs with high throughput and optimized inference latency using vLLM's PagedAttention and continuous batching. This approach targets production deployment by efficiently managing memory and processing concurrent requests.

What's the best way to serve large language models with limited GPU memory?▼

Serve large language models with limited GPU memory by applying quantization techniques like GPTQ, AWQ, or FP8. You can also enable tensor parallelism to distribute the model across multiple GPUs.

Can I use an OpenAI-compatible API endpoint for my production LLM deployment?▼

Yes, you can use an OpenAI-compatible API endpoint for production LLM deployment. This compatibility allows you to easily integrate the served models with existing applications and tools expecting OpenAI API formats.

How does PagedAttention optimize inference latency for LLM serving?▼

PagedAttention optimizes inference latency by managing attention keys and values in non-contiguous memory blocks. This mechanism reduces memory waste and enables continuous batching for higher throughput.

Do I need PyTorch and Transformers to run vLLM for model inference?▼

Yes, you need PyTorch and Transformers, along with vLLM, to run model inference. These dependencies provide the underlying deep learning framework and model loading capabilities required for serving.

How do I deploy a production LLM API using vLLM?▼

Deploy a production LLM API using vLLM by running the serve command with your target model, such as `vllm serve meta-llama/Llama-3-8B-Instruct`. This initiates an optimized endpoint ready for inference.

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