The Kaitchup – AI on a Budget

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The Kaitchup – AI on a Budget
The Kaitchup – AI on a Budget
GuideLLM: Is Your Server Ready for LLM Deployment?

GuideLLM: Is Your Server Ready for LLM Deployment?

Simulate real-world inference workloads with GuideLLM

Benjamin Marie's avatar
Benjamin Marie
Sep 12, 2024
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The Kaitchup – AI on a Budget
The Kaitchup – AI on a Budget
GuideLLM: Is Your Server Ready for LLM Deployment?
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GuideLLM User Flows
Illustration by Neural Magic — source

We have numerous scripts and utilities available to benchmark the latency and inference throughput of large language models (LLMs). vLLM, TGI, and llama.cpp can all tell you how fast an LLM is on your machine. However, they are not designed to evaluate how well your server can handle real-world scenarios involving multiple simultaneous queries from users.

How can you determine if your server is robust enough to manage the demands of real-world inference workloads?

This is where GuideLLM is useful. Developed by Neural Magic, GuideLLM is a framework designed to evaluate LLM deployment by simulating real-world workloads under different load conditions. It helps you assess how your server handles concurrent or synchronous queries.

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In this article, I will introduce GuideLLM and walk you through its key features. Next, I will explain how to install and run the framework, as well as how to interpret the performance reports it generates. To provide practical examples, I used GuideLLM to evaluate two different server configurations provided by RunPod (referral link):

  1. A vLLM server running Llama 3.1 8B Instruct, powered by an A40 GPU (48 GB of VRAM).

  2. A vLLM server running Qwen2-1.5B Instruct, powered by an RTX 3090 GPU (24 GB of VRAM).

Through these examples, we will understand how GuideLLM can assess and compare the performance of different LLM setups.

The notebook implementing simple examples to generate reports with GuideLLM is here:

Get the notebook (#103)

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