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serving-llms-vllm

Serve large language models with quantization, tensor parallelism, and OpenAI-compatible endpoints.

Updated Jun 9, 2026
Search Tags:#OpenAI#model deployment#quantization#large language models#high-throughput#tensor parallelism#inference serving
One-click install
npx skills add https://github.com/zhouboyu-xreal/Hermes-Memory --skill serving-llms-vllm-zhouboyu-xreal
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: serving-llms-vllm
Source: https://github.com/zhouboyu-xreal/Hermes-Memory/tree/main/skills/mlops/inference/vllm
Command: npx skills add https://github.com/zhouboyu-xreal/Hermes-Memory --skill serving-llms-vllm-zhouboyu-xreal

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill solves the problem of deploying and serving large language models at high throughput while managing inference latency and limited GPU memory efficiently.

Core Features & Use Cases

  • High Throughput Inference: Achieve up to 24x higher throughput than standard transformers.
  • OpenAI API Compatibility: Serve models via OpenAI-compatible endpoints.
  • Quantization and Parallelism: Offers quantization methods (GPTQ/AWQ/FP8) and tensor parallelism to enhance efficiency and resource management.
  • Production Grade Serving: Built for high reliability in production environments.
  • Use Case: A business seeking a fast, accurate LLM service that requires high throughput, precision, and resource-efficient deployment can benefit significantly from vLLM.

Quick Start

Serve the 'meta-llama/Llama-3-8B-Instruct' model on your GPU:

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 for production inference?▼

Serve large language models with high throughput using quantization, tensor parallelism, and optimized memory management to achieve up to 24x higher inference throughput than standard transformers.

Can I expose my served models using an OpenAI compatible API endpoint?▼

Yes, you can expose served models using an OpenAI compatible API endpoint, enabling seamless integration for applications requiring high inference throughput with large model architectures.

What quantization methods are available for efficient LLM inference serving?▼

Available quantization methods for efficient LLM inference serving include GPTQ, AWQ, and FP8, which enhance efficiency and manage limited GPU memory during deployment.

Does tensor parallelism help manage GPU memory when deploying large language models?▼

Tensor parallelism helps manage GPU memory by distributing large model architectures across multiple GPUs, enhancing resource management and maintaining high throughput inference.

How do I start serving a Llama model on my GPU?▼

Start serving a Llama model on your GPU by running the quick start command `vllm serve meta-llama/Llama-3-8B-Instruct` to initialize resource-efficient inference.

Why is my large language model deployment running out of GPU memory?▼

Large language model deployments run out of GPU memory due to unoptimized memory management; applying quantization techniques like GPTQ or AWQ resolves this by reducing the memory footprint.

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