Posts by Zhenyu Gu
Resilient Large-Scale Training: Integrating TorchFT with TorchTitan on AMD GPUs
- 08 February 2026
Training large AI models on AMD GPUs demands unwavering stability and robust fault-tolerance capabilities at cluster scale. Yet today’s ROCm-based multi-node GPU deployments often rely on brittle checkpoint-and-restart mechanisms to recover from failures. This approach wastes precious compute cycles and slows down training as model sizes and cluster scales grow. To address these challenges, we integrated PyTorch’s native fault-tolerance framework—TorchFT—with the TorchTitan training framework on AMD’s Primus-SaFE Kubernetes platform, achieving resilient, checkpoint-less training at hundred-GPU scale. This blog builds upon our previous work on the Primus ecosystem—for background on the platform architecture, see our earlier posts on Primus-SaFE, the Primus training framework, and training large models with Primus.
MoE Training Best Practices on AMD GPUs
- 16 December 2025
This blog covers best practices for training Mixture-of-Experts (MoE) models on AMD Instinct™ MI300/MI355-series[a] GPUs with the ROCm ecosystem. Whether you’re new to MoE distributed architectures or optimizing trillion-parameter models, this guide will help you identify bottlenecks and maximize efficiency on AMD hardware.
Stability at Scale: AMD’s Full‑Stack Platform for Large‑Model Training
- 04 November 2025
Training large AI models on AMD GPUs demands unwavering stability and robust debugging capabilities at cluster scale. Yet today’s ROCm-based multi-node GPU deployments often rely on brittle scripts and disjointed tools to launch distributed jobs, monitor performance, and recover from failures. This patchwork approach makes troubleshooting difficult and undermines cluster-wide reliability as model sizes and run times grow.
An Introduction to Primus-Turbo: A Library for Accelerating Transformer Models on AMD GPUs
- 19 September 2025
With the rapid growth of large-scale models, acceleration libraries are facing higher demands: they must deliver exceptional performance, offer comprehensive functionality, and remain easy to use. To meet these needs, we introduce Primus-Turbo — part of the Primus product family (see our previous blog for background). Primus-Turbo is designed around three core principles: performance, completeness, and ease of use. It supports training, inference, and a wide range of application scenarios, providing developers with a solid foundation to efficiently build and optimize large models on the ROCm platform. See Figure 1 below for a comprehensive stack coverage of Primus-Turbo.
Day 0 Developer Guide: Running the Latest Open Models from OpenAI on AMD AI Hardware
- 05 August 2025
OpenAI has officially released its open models: gpt-oss-120b and gpt-oss-20b. AMD now provides out-of-the-box, day 0 support for the latest open models from OpenAI, enabling developers to easily fine-tune and deploy across cloud to client environments using AMD hardware, the AMD ROCm™ and AMD Ryzen™ AI software stack, and seamless open source integrations. At AMD, we’re excited to announce day 0 support across our AI hardware, including our flagship AMD Instinct™ MI355X and MI300X GPUs, AMD Radeon™ AI PRO R9700 GPUs, and AMD Ryzen™ AI processors.