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  • Deploying vLLM models on Azure Machine Learning with Managed Online . . .
    In this post, we’ve discussed how to deploy vLLM models using Azure Machine Learning’s Managed Online Endpoints for efficient real-time inference We introduced vLLM as a high-throughput, memory-efficient inference engine for LLMs, with the focus of deploying models from HuggingFace
  • How to deploy and inference a managed compute deployment with code
    Deployment typically involves hosting the model on a server or in the cloud and creating an API or other interface for users to interact with the model You can invoke the deployment for real-time inference of generative AI applications such as chat and copilot
  • How to deploy an open-source code LLM for your dev team
    Text Generation Inference (TGI) is an open-source toolkit for deploying and serving LLMs It is designed for fast inference and high throughput, enabling you to provide a highly concurrent, low latency experience As of October 2023, TGI has been optimized for Code Llama, Mistral, StarCoder, and Llama 2 on NVIDIA A100, A10G and T4 GPUs
  • Azure Kubernetes Service — production-stack - docs. vllm. ai
    Run the deployment script by replacing RESOURCE_GROUP and YAML_FILE_PATH with the actual values: After executing the script, Kubernetes will start deploying the vLLM inference stack You can monitor the status of the deployment 2 Validate Installation # To check whether the pods for vLLM deployment are up and running, use: Expected output:
  • Cost-Effective Private Large Language Model Inference on Azure . . .
    Deploying large language models for inference is compute-intensive, requiring high-end GPUs and specialized hardware that can be very expensive to provision on cloud platforms For example, an 80GB Nvidia A100 GPU on Azure can generate approximately 60 completion tokens per second when running a 13 billion parameter model like Vicuna
  • Deploying vLLM: a Step-by-Step Guide - Ploomber
    vLLM is an open-source project that allows you to do LLM inference and serving Inference means that you can download model weights and pass them to vLLM to perform inference via their Python API; here’s an example from their documentation:
  • Deploying Language Models on Azure Kubernetes: A . . . - Hugging Face
    Deploying Language Models on Azure Kubernetes Service (AKS) A Detailed Step-by-Step Implementation Guide Introduction This comprehensive guide explains how to deploy Large Language Models (LLMs) on Azure Kubernetes Service using the vLLM serving engine
  • vLLM OpenAI on Azure — Stack Templates — Northflank
    Deploy vLLM on Azure with Northflank, a high-performance serving engine for Large Language Models (LLMs) vLLM serves models with an OpenAI-compatible API endpoint, allowing you to seamlessly integrate and interact with models using familiar OpenAI API patterns and tooling
  • Model Inference with AMD Instinct MI300X on Azure Using vLLM
    This tutorial demonstrates how to run model inference workloads using AMD Instinct MI300X GPUs on Microsoft Azure with vLLM, a popular library for LLM inference





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