serving-llms-vllm

Deploy OpenAI-compatible LLM inference endpoints using vLLM with PagedAttention and continuous batching.

Updated Aug 23, 2026

Search Tags:#quantization #openai-api #vllm #pagedattention #llm-serving #tensor-parallelism #continuous-batching

Ready to run

npx skills add https://github.com/box755/simlens-research --skill serving-llms-vllm-box755

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: serving-llms-vllm
Source: https://github.com/box755/simlens-research/tree/main/skill-packs/AI-Research-SKILLs/12-inference-serving/vllm
Command: npx skills add https://github.com/box755/simlens-research --skill serving-llms-vllm-box755

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps you deploy and run large language models efficiently by providing high-throughput inference serving using vLLM, reducing latency and GPU memory pressure for production workloads.

Core Features & Use Cases

High-throughput LLM serving: Uses vLLM’s PagedAttention and continuous batching to mix prefill and decode requests for better overall throughput, making it suitable for high-request workloads like chat and assistant backends.
OpenAI-compatible endpoints: Exposes an OpenAI-style /v1 server interface so you can reuse existing OpenAI SDK or client integrations.
Memory- and cost-efficient deployment: Supports quantization (AWQ/GPTQ/FP8) and tensor parallelism so large models can run with limited GPU memory and across multiple GPUs.

Quick Start

Install vLLM and start an OpenAI-compatible server for your model by running: vllm serve meta-llama/Llama-3-8B-Instruct --port 8000 --gpu-memory-utilization 0.9

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 an OpenAI-compatible API?▼

You can serve LLMs with an OpenAI-compatible API by deploying vLLM to expose an OpenAI-style /v1 server interface, allowing you to reuse existing OpenAI SDK or client integrations for high-throughput inference.

How does PagedAttention and continuous batching improve LLM serving throughput?▼

PagedAttention and continuous batching improve LLM serving throughput by mixing prefill and decode requests, which optimizes GPU memory utilization and reduces latency for multi-user chatbot and batch generation workloads.

Can I run large models across multiple GPUs using tensor parallelism in vLLM?▼

Yes, you can run large models across multiple GPUs using tensor parallelism in vLLM, which allows you to distribute model execution to handle models that exceed single-GPU memory limits.

Does vLLM support quantization formats like AWQ, GPTQ, and FP8?▼

Yes, vLLM supports optional quantization formats including AWQ, GPTQ, and FP8, enabling memory- and cost-efficient deployment of large language models with limited GPU memory.

How do I start an OpenAI-compatible vLLM server for a local model?▼

To start an OpenAI-compatible vLLM server, run the command `vllm serve` with your model name, specifying port and GPU memory utilization, such as `vllm serve meta-llama/Llama-3-8B-Instruct --port 8000 --gpu-memory-utilization 0.9`.

What is the best way to reduce time-to-first-token for production LLM workloads?▼

The best way to reduce time-to-first-token for production LLM workloads is to use vLLM's PagedAttention and continuous batching, which optimize prefill and decode execution for high tokens-per-second performance.