Distributed Inference with Multi-Machine & Multi-GPU Setup | Deploying Large Models via vLLM & Ray !

Автор: sheepcraft7555

Загружено: 2024-09-19

Просмотров: 4055

Описание: Discover how to set up a distributed inference endpoint using a multi-machine, multi-GPU configuration to deploy large models that can't fit on a single machine or to increase throughput across machines. This tutorial walks you through the critical parameters for hosting inference workloads using vLLM and Ray, keeping things streamlined without diving too deep into the underlying frameworks. Whether you're dealing with ultra-large models or scaling your inference infrastructure, this guide will help you maximize efficiency across nodes. Don't forget to check out my previous videos on distributed training for more insights into handling large-scale ML tasks.

Key Topics Covered:
1. Multi-GPU, multi-node distributed inference setup
2. Scaling inference beyond a single machine
3. Essential parameters for vLLM and Ray integration
4. Practical tips for deploying large models

#DistributedInference #MultiGPU #AIInference #vLLM #Ray #MLInfrastructure #ScalableAI #machinelearning #gpu #deeplearning #llm #largelanguagemodels #artificialintelligence #vllm #ray #inference #distributeddeeplearning

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Distributed Inference with Multi-Machine & Multi-GPU Setup | Deploying Large Models via vLLM & Ray !

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