filmov
tv
Distributed Inference with Multi-Machine & Multi-GPU Setup | Deploying Large Models via vLLM & Ray !
Показать 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
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
0:27:35
Distributed Inference with Multi-Machine & Multi-GPU Setup | Deploying Large Models via vLLM &am...
0:16:45
Run A Local LLM Across Multiple Computers! (vLLM Distributed Inference)
0:46:24
LocalAI LLM Testing: Distributed Inference on a network? Llama 3.1 70B on Multi GPUs/Multiple Nodes
0:41:49
Distributed Multi-Node Model Inference Using the LeaderWorkerSet API- Abdullah Gharaibeh, Rupeng Liu
0:08:02
NVIDIA Dynamo - LLM Inference in Multi-Node Distributed Environments
0:07:58
What is Mixture of Experts?
0:30:52
The Evolution of Multi-GPU Inference in vLLM | Ray Summit 2024
0:48:20
vLLM Office Hours - Distributed Inference with vLLM - January 23, 2025
0:03:13
Nvidia CUDA in 100 Seconds
0:04:02
Unit 9.2 | Multi-GPU Training Strategies | Part 1 | Introduction to Multi-GPU Training
1:29:18
Introducing NVIDIA Dynamo: A Distributed Inference Serving Framework for Reasoning models
0:03:16
Part 2: What is Distributed Data Parallel (DDP)
0:18:06
M4 Mac Mini CLUSTER 🤯
0:09:41
Ray: Faster Python through parallel and distributed computing
0:05:29
Distributed Inference 101: Managing KV Cache to Speed Up Inference Latency
0:34:57
I built an AI supercomputer with 5 Mac Studios
0:11:22
Cheap mini runs a 70B LLM 🤯
0:01:08
Accelerate Big Model Inference: How Does it Work?
0:01:00
A Hardware Prototype Targeting Distributed Deep Learning for On-Device Inference
0:01:57
Part 1: Welcome to the Distributed Data Parallel (DDP) Tutorial Series
0:00:41
Why AI Inference on Hathora Just Makes Sense
0:00:33
The Biggest Challenge with Multi-Agent AI Systems Explained
0:23:20
Inference Risks for Machine Learning (ICLR Workshop on Distributed and Private Machine Learning)
0:13:00
Using Clusters to Boost LLMs 🚀
Комментарии