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serving-llms-vllm

Serve OpenAI-compatible LLM APIs with optimized throughput and quantization.

1|1|Updated May 9, 2026
Search Tags:#quantization#latency reduction#inference serving#large language model#throughput optimization
One-click install
npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill serving-llms-vllm-ldzhhxx
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: serving-llms-vllm
Source: https://github.com/ldzhhxx/Hermes_offline_v2/tree/main/hermes-agent/skills/mlops/inference/vllm
Command: npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill serving-llms-vllm-ldzhhxx

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 solves the challenge of efficiently deploying production LLM APIs, optimizing inference latency/throughput, and serving models with limited GPU memory.

Core Features & Use Cases

  • High Throughput: Achieves 24x higher throughput than standard transformers.
  • OpenAI API Compatibility: Supports OpenAI-compatible endpoints.
  • Quantization: Supports quantization (GPTQ/AWQ/FP8) to save GPU memory.
  • Tensor Parallelism: Supports tensor parallelism for large models.
  • Use Cases: Ideal for multi-user applications (chatbots, assistants) that require low latency and high throughput, serving OpenAI-compatible endpoints, and large models with limited GPU memory.

Quick Start

Install the vLLM package using pip install vllm. Then, you can use it for basic offline inference as follows:

from vllm import LLM, SamplingParams
llm = LLM(model="meta-llama/Llama-3-8B-Instruct")
sampling = SamplingParams(temperature=0.7, max_tokens=256)
outputs = llm.generate(["Explain quantum computing"], sampling)
print(outputs[0].outputs[0].text)

Frequently Asked Questions about serving-llms-vllm

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

FAQPage Schema
How do I optimize large language model inference throughput for multi-user applications?▼

You can reduce large language model inference latency by deploying production-grade APIs with this Skill, which supports OpenAI-compatible endpoints and is designed for multi-user applications requiring low latency and high throughput.

How do I serve a large language model with limited GPU memory?▼

You can serve large models with limited GPU memory using this Skill by applying quantization techniques like GPTQ, AWQ, or FP8 to reduce memory footprint, alongside tensor parallelism for multi-GPU deployment.

How do I set up an OpenAI-compatible API endpoint for my large language model?▼

You can set up an OpenAI-compatible API endpoint by using this Skill to serve production-grade LLM APIs, supporting OpenAI-compatible endpoints for seamless integration with chatbots and assistants.

Does vllm work with torch and transformers for offline inference?▼

Yes, vllm works with torch and transformers for offline inference, allowing you to load models like Llama-3-8B-Instruct and generate text by configuring sampling parameters such as temperature and max tokens.

What is the best way to reduce inference latency when deploying LLM APIs?▼

The best way to reduce inference latency when deploying LLM APIs is using this Skill, which provides high-performance serving with optimized throughput and latency, ideal for multi-user applications with high inference load.

What are the limitations of using quantization for large language model serving?▼

While quantization helps save GPU memory for large language model serving, you must ensure your model supports GPTQ, AWQ, or FPQ formats, and consider potential accuracy trade-offs when deploying high-throughput multi-user applications.

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