Applications & models - Page 8#
Explore the latest blogs about applications and models in the ROCm ecosystem, including machine learning frameworks, AI models, and application case studies.
Coding Agents on AMD GPUs: Fast LLM Pipelines for Developers
Accelerate AI-assisted coding with agentic workflows on AMD GPUs. Deploy DeepSeek-V3.1 via SGLang, vLLM, or llama.cpp to power fast, scalable coding agents
Day-0 Support for the SGLang-Native RL Framework - slime on AMD Instinct™ GPUs
Learn how to deploy slime on AMD GPUs for high-performance RL training with ROCm optimization
Accelerating Audio-Driven Video Generation: WAN2.2-S2V on AMD ROCm
This blog will highlight AMD ROCm’s ability to power next-generation audio-to-video models with simple, reproducible workflows.
A Simple Design for Serving Video Generation Models with Distributed Inference
Minimalist FastAPI + Redis + Torchrun design for serving video generation models with distributed inference.
Optimizing Drug Discovery Tools on AMD MI300X Part 1: Molecular Design with REINVENT
Learn how to set up, run, and optimize REINVENT4, a molecular design tool, on AMD MI300X GPUs for faster drug discovery workflows
Running SOTA AI-based Weather Forecasting models on AMD Instinct
We look at a few State of the Art AI models in weather forecasting, and demonstrate how to run them on AMD Instinct MI300X in a step-by-step fashion.
AMD-HybridLM: Towards Extremely Efficient Hybrid Language Models
Explore AMD-HybridLM’s architecture and see how hybridization redefines LLM efficiency and performance without requiring retraining from scratch
Exploring Use Cases for Scalable AI: Implementing Ray with ROCm Support for Efficient ML Workflows
Ray, combined with ROCm, provides a powerful platform for scaling AI applications, particularly for training and inference workloads.
Technical Dive into AMD's MLPerf Inference v5.1 Submission
In this blog, we share the technical details of how we accomplish the results in our MLPerf Inference v5.1 submission.
Reproducing the AMD Instinct™ GPUs MLPerf Inference v5.1 Submission
In this blog, we will provide step by step instruction on how to reproduce AMD's MLPerf Inference v5.1 Submission
Slim Down Your Llama: Pruning & Fine-Tuning for Maximum Performance
This blog describes the technical details of how we prune and fine tune the Llama 3.1 405B model in our MLPerf Inference v5.1 submission.
Step-3 Deployment Simplified: A Day 0 Developer’s Guide on AMD Instinct™ GPUs
Learn how to deploy Step-3, a 321B-parameter VLM with MFA & AFD, on AMD Instinct™ GPUs to cut decoding costs and boost long-context reasoning