# Hugging Face — Efficient Request Queueing – Optimizing LLM Performance

- Company: Hugging Face (huggingface.co)
- Announced: 2025-04-02T13:33:53+00:00
- Subject: Platform
- Source: https://huggingface.co/blog/tngtech/llm-performance-request-queueing
- Record: https://forck.live/items/1721-efficient-request-queueing-optimizing-llm-performance

The article discusses the problem of request queuing in LLM inference engines and presents a solution using fair scheduling with separate queues per user and model, and dynamic rate limiting to keep the backend queue short.

## Evidence

Verbatim from https://huggingface.co/blog/tngtech/llm-performance-request-queueing:

> Serving LLMs to many applications and users in parallel is challenging because they compete for limited GPU resources. This article is the first in a series on LLM performance, based on our experience with serving self-hosted LLMs at TNG Technology Consulting GmbH. In the first part, we focus on the impact of queuing and discuss different scheduling strategies.

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Record: https://forck.live/items/1721-efficient-request-queueing-optimizing-llm-performance
Catalogue: https://forck.live/llms.txt
Feed: https://forck.live/feed.md