AI - Applications & Models - Page 9#
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.
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
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.
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.
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
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.
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.