Recent Posts - Page 4#

Deploying Serverless AI Inference on AMD GPU Clusters
This blog helps targeted audience in setting up AI inference serverless deployment in a kubernetes cluster with AMD accelerators. Blog aims to provide a comprehensive guide for deploying and scaling AI inference workloads on serverless infrastructre.

Unlock DeepSeek-R1 Inference Performance on AMD Instinct™ MI300X GPU
This blog introduces the key performance optimizations made to enable DeepSeek-R1 Inference

How to Build a vLLM Container for Inference and Benchmarking
This post, the second in a series, provides a walkthrough for building a vLLM container that can be used for both inference and benchmarking.

Fine-tuning Phi-3.5-mini LLM at scale: Harnessing Accelerate and Slurm for multinode training
Fine-tuning Phi-3.5-mini-instruct LLM using multinode distributed training with Hugging Face Accelerate, Slurm, and Docker for scalable efficiency.

AI Inference Orchestration with Kubernetes on Instinct MI300X, Part 2
This blog is part 2 of a series aimed at providing a comprehensive, step-by-step guide for deploying and scaling AI inference workloads with Kubernetes and the AMD GPU Operator on the AMD Instinct platform

Understanding Peak, Max-Achievable & Delivered FLOPs, Part 1
Understanding Peak, Max-Achievable & Delivered FLOPs

Navigating vLLM Inference with ROCm and Kubernetes
Quick introduction to Kubernetes (K8s) and a step-by-step guide on how to use K8s to deploy vLLM using ROCm.

PyTorch Fully Sharded Data Parallel (FSDP) on AMD GPUs with ROCm
This blog guides you through the process of using PyTorch FSDP to fine-tune LLMs efficiently on AMD GPUs.

Deep dive into the MI300 compute and memory partition modes
This blog explains how to use the MI300 compute and memory partitioning modes to optimize your performance-critical applications.

MI300A - Exploring the APU advantage
This blog post introduces the MI300 APU hardware, how it differs from other discrete systems, and how to leverage its GPU programming

AI Inference Orchestration with Kubernetes on Instinct MI300X, Part 1
This blog is part 1 of a series aimed at providing a comprehensive, step-by-step guide for deploying and scaling AI inference workloads with Kubernetes and the AMD GPU Operator on the AMD Instinct platform

GEMM Kernel Optimization For AMD GPUs
Guide to how GEMMs can be tuned for optimal performance of AI models on AMD GPUs