Recent Posts - Page 3#
Supercharge DeepSeek-R1 Inference on AMD Instinct MI300X
Learn how to optimize DeepSeek-R1 on AMD MI300X with SGLang, AITER kernels and hyperparameter tuning for up to 5× throughput and 60% lower latency over Nvidia H200
AITER: AI Tensor Engine For ROCm
We introduce AMD's AI Tensor Engine for ROCm (AITER), our centralized high performance AI operators repository, designed to significantly accelerate AI workloads on AMD GPUs
Deploying Google’s Gemma 3 Model with vLLM on AMD Instinct™ MI300X GPUs: A Step-by-Step Guide
AMD is excited to announce the integration of Google’s Gemma 3 models with AMD Instinct™ MI300X GPUs
Analyzing the Impact of Tensor Parallelism Configurations on LLM Inference Performance
This blog analyzes how tensor parallelism impacts TCO and Scale for LLM deployments in production.
AI Inference Orchestration with Kubernetes on Instinct MI300X, Part 3
This blog is part 3 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
Optimized ROCm Docker for Distributed AI Training
AMD updated Docker images incorporate torchtune finetuning, FP8 support, single node performance boost, bug fixes & updated benchmarking for stable, efficient distributed training
AMD Advances Enterprise AI Through OPEA Integration
We announce AMD’s support of Open Platform for Enterprise AI (OPEA), integrating OPEA’s enterprise GenAI framework with AMD’s computing hardware and ROCm software
Instella-VL-1B: First AMD Vision Language Model
We introduce Instella-VL-1B, the first AMD vision language model for image understanding trained on MI300X GPUs, outperforming fully open-source models and matching or exceeding many open-weight counterparts in general multimodal benchmarks and OCR-related tasks.
Introducing Instella: New State-of-the-art Fully Open 3B Language Models
AMD is excited to announce Instella, a family of fully open state-of-the-art 3-billion-parameter language models (LMs). , In this blog we explain how the Instella models were trained, and how to access them.
Understanding RCCL Bandwidth and xGMI Performance on AMD Instinct™ MI300X
The blog explains the reasons behind RCCL bandwidth limitations and xGMI performance constraints, and provides actionable steps to maximize link efficiency on AMD MI300X
Measuring Max-Achievable FLOPs – Part 2
AMD measures Max-Achievable FLOPS through controlled benchmarking: real-world data patterns, thermally stable devices, and cold cache testing—revealing how actual performance differs from theoretical peaks.
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.