ROCm Blogs#
What's New in the AMD GPU Operator v1.2.0 Release
This blog highlights the new feature enhancements that were released as part of the AMD GPU Operator v1.2.0 release. New features that enhance the use of AMD Instinct GPUs on Kubernetes including Automated Upgrades, Health Checks and Open-sourcing the codebase.
Bring FLUX to Life on MI300X: Run and Optimize with Hugging Face Diffusers
The blog will walk you through the FLUX text-to-image diffusion model architecture and show you how to run and optimize it on MI300x.
Accelerating LLM Inference: Up to 3x Speedup on MI300X with Speculative Decoding
This blog demonstrates out-of-the-box performance improvement in LLM inference using speculative decoding on MI300X.
Introducing ROCprofiler SDK - The Latest Toolkit for Performance Profiling
Discover ROCprofiler SDK – ROCm’s next-generation, unified, scalable, and high-performance profiling toolkit for AI and HPC workloads on AMD GPUs.
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
Boosting Computational Fluid Dynamics Performance with AMD Instinct™ MI300X
The blog introduces CFD Ansys Fluent benchmarks and provides hands-on guide on installing and running four different Fluent models on AMD GPUs using ROCm.
Training Transformers and Hybrid models on AMD Instinct MI300X Accelerators
This blog shows Zyphra's new training kernels for transformers and hybrid models on AMD Instinct MI300X accelerators, surpassing the H100s performance
Introducing AMD's Next-Gen Fortran Compiler
In this post we present a brief preview of AMD's Next-Gen Fortran Compiler, our new open source Fortran complier optimized for AMD GPUs using OpenMP offloading, offering direct interface to ROCm and HIP.
Efficient MoE training on AMD ROCm: How-to use Megablocks on AMD GPUs
Learn how to use Megablocks to pre-train GPT2 Mixture of Experts (MoE) model, helping you scale your deep learning models effectiveness on AMD GPUs using ROCm
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.
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
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.
Speculative Decoding - Deep Dive
This blog shows the performance improvement achieved by applying speculative decoding with Llama models on AMD MI300X GPUs, tested across models, input sizes, and datasets.
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
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
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
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