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Model Deployment: vLLM, TGI, ONNX, Quantization, GPU Optimization

Introduction

Deploying large language models for production inference requires specialized infrastructure. Unlike traditional ML models, LLMs demand gigabytes of GPU memory, specialized attention kernels, and careful batching strategies to achieve acceptable throughput. This article covers the major deployment frameworks and optimization techniques.

vLLM

vLLM is the most popular open-source LLM serving framework, featuring PagedAttention for efficient memory management:

# Using vLLM's OpenAI-compatible API server

# Start server:

# python -m vllm.entrypoints.openai.api_server \

# --model meta-llama/Llama-3.1-8B-Instruct \

# --tensor-parallel-size 2 \

# --gpu-memory-utilization 0.95 \

# --max-model-len 8192 \

# --dtype bfloat16

from openai import OpenAI

client =…