AI Blogs - Page 12#
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.
Measuring Max-Achievable FLOPs – Part 2
AMD measures Max-Achievable FLOPS through controlled benchmarking: real-world data patterns, thermally stable devices, and cold cache testing—revealing how actual performance differs from theoretical peaks.
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
How to Build a vLLM Container for Inference and Benchmarking
This post, the second in a series, provides a walkthrough for building a vLLM container that can be used for both inference and benchmarking.
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.
AI Inference Orchestration with Kubernetes on Instinct MI300X, Part 2
This blog is part 2 of a series aimed at providing a comprehensive, step-by-step guide for deploying and scaling AI inference workloads with Kubernetes and the AMD GPU Operator on the AMD Instinct platform
Understanding Peak, Max-Achievable & Delivered FLOPs, Part 1
Understanding Peak, Max-Achievable & Delivered FLOPs
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.
AI Inference Orchestration with Kubernetes on Instinct MI300X, Part 1
This blog is part 1 of a series aimed at providing a comprehensive, step-by-step guide for deploying and scaling AI inference workloads with Kubernetes and the AMD GPU Operator on the AMD Instinct platform