Vish Vadlamani#
Vish Vadlamani is a Senior Director of Software Engineering at AMD. He and his team are responsible for all AI SDK Vertical, Horizontal and Ecosystem components. He has passion in the AI and cloud computing space with over 3 decades of software development and management experience. Vish did his management coursework from Harvard, Masters in Engineering from UT Austin and a B.Tech in Engineering from IIT Bombay.
Posts by Vish Vadlamani
Announcing MONAI 1.0.0 for AMD ROCm: Breakthrough AI Acceleration for Medical Imaging Models on AMD Instinct™ GPUs
Learn how to use Medical Open Network for Artificial Intelligence (MONAI) 1.0 on ROCm, with examples and demonstrations.
Elevating 3D Scene Rendering with GSplat
ROCm Port of GSplat - GPU accelerated rasterization of Gaussian splatting
From Ingestion to Inference: RAG Pipelines on AMD GPUs
Build a RAG enhanced GenAI application that improves the quality of model responses by incorporating data that is missing in the model training data.
Enabling FlashInfer on ROCm for Accelerated LLM Serving
FlashInfer is an open-source library for accelerating LLM serving that is now supported by ROCm.
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
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.
Llama.cpp Meets Instinct: A New Era of Open-Source AI Acceleration
performance optimizations for llama.cpp on AMD Instinct GPUs
DGL in the Real World: Running GNNs on Real Use Cases
We walk through four advanced GNN workloads from heterogeneous e-commerce graphs to neuroscience applications that we successfully ran using our DGL implementation.
Accelerating Parallel Programming in Python with Taichi Lang on AMD GPUs
This blog provides a how-to guide on installing and programming with Taichi Lang on AMD Instinct GPUs.
Graph Neural Networks at Scale: DGL with ROCm on AMD Hardware
Accelerate Graph Deep Learning on AMD GPUs with DGL and ROCm—scale efficiently with open tools and optimized performance.
Reinforcement Learning from Human Feedback on AMD GPUs with verl and ROCm Integration
Deploy verl on AMD GPUs for fast, scalable RLHF training with ROCm optimization, Docker scripts, and impressive throughput-convergence results
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
Triton Inference Server with vLLM on AMD GPUs
This blog provides a how-to guide on setting up a Triton Inference Server with vLLM backend powered by AMD GPUs, showcasing robust performance with several LLMs