Recent Posts - Page 7#
Transformer based Encoder-Decoder models for image-captioning on AMD GPUs
The blog introduces image captioning and provides hands-on tutorials on three different Transformer-based encoder-decoder image captioning models: ViT-GPT2, BLIP, and Alpha- CLIP, deployed on AMD GPUs using ROCm.
SGLang: Fast Serving Framework for Large Language and Vision-Language Models on AMD Instinct GPUs
Discover SGLang, a fast serving framework designed for large language and vision-language models on AMD GPUs, supporting efficient runtime and a flexible programming interface.
Quantized 8-bit LLM training and inference using bitsandbytes on AMD GPUs
Learn how to use bitsandbytes’ 8-bit representations techniques, 8-bit optimizer and LLM.int8, to optimize your LLMs training and inference using ROCm on AMD GPUs
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.
Distributed Data Parallel Training on AMD GPU with ROCm
This blog demonstrates how to speed up the training of a ResNet model on the CIFAR-100 classification task using PyTorch DDP on AMD GPUs with ROCm.
Torchtune on AMD GPUs How-To Guide: Fine-tuning and Scaling LLMs with Multi-GPU Power
Torchtune is a PyTorch library that enables efficient fine-tuning of LLMs. In this blog we use Torchtune to fine-tune the Llama-3.1-8B model for summarization tasks using LoRA and showcasing scalable training across multiple GPUs.
CTranslate2: Efficient Inference with Transformer Models on AMD GPUs
Optimizing Transformer models with CTranslate2 for efficient inference on AMD GPUs
Inference with Llama 3.2 Vision LLMs on AMD GPUs Using ROCm
Meta's Llama 3.2 Vision models bring multimodal capabilities for vision-text tasks. This blog explores leveraging them on AMD GPUs with ROCm for efficient AI workflows.
Speed Up Text Generation with Speculative Sampling on AMD GPUs
This blog will introduce you to assisted text generation using Speculative Sampling. We briefly explain the principles underlying Speculative Sampling and demonstrate its implementation on AMD GPUs using ROCm.
Multinode Fine-Tuning of Stable Diffusion XL on AMD GPUs with Hugging Face Accelerate and OCI's Kubernetes Engine (OKE)
This blog demonstrates how to set-up and fine-tune a Stable Diffusion XL (SDXL) model in a multinode Oracle Cloud Infrastructure’s (OCI) Kubernetes Engine (OKE) on a cluster of AMD GPUs using ROCm
Enhancing vLLM Inference on AMD GPUs
In this blog, we’ll demonstrate the latest performance enhancements in vLLM inference on AMD Instinct accelerators using ROCm. In a nutshell, vLLM optimizes GPU memory utilization, allowing more efficient handling of large language models (LLMs) within existing hardware constraints, maximizing throughput and minimizing latency.
Supercharging JAX with Triton Kernels on AMD GPUs
In this blog post we guide you through developing a fused dropout activation kernel for matrices in Triton, calling the kernel from JAX, and benchmarking its performance.