Writing · Ramakrishnan Sivakumar

AMD Developer Blog

Day 0 Support for Gemma 4 on AMD Processors and GPUs

How AMD delivered day-0 support for Google's Gemma 4 across Ryzen AI processors and Radeon GPUs, so the model runs locally through Lemonade the day it ships.

Lemonade by AMD: A Unified API for Local AI Developers

The flagship introduction to Lemonade as AMD's answer to fragmented local AI: one API surface over heterogeneous engines and hardware, from Ryzen AI NPUs to Radeon GPUs.

Ryzen AI and Radeon are ready to run LLMs Locally with Lemonade Software

A practical guide to running large language models locally on AMD hardware: Lemonade as the serving platform that picks the best available acceleration, from NPU to integrated and discrete GPUs, behind the OpenAI API standard.

Run OpenAI's gpt-oss locally with Lemonade

Day-0 guide to running OpenAI's open-weight gpt-oss models fully locally with Lemonade - co-authored with the Lemonade maintainer team.

Rethinking Local AI: Lemonade Server's Python Advantage

Why Lemonade Server is built in Python and what that unlocks: rapid engine integration, a hackable stack for the community, and no performance penalty where it matters - the inference engines underneath.

Local Tiny Agents: MCP Agents on Ryzen AI with Lemonade Server

Running Model Context Protocol agents entirely locally: Hugging Face Tiny Agents driven by NPU-accelerated LLMs on Ryzen AI through Lemonade Server - agentic AI without the cloud.

Unlocking a Wave of LLM Apps on Ryzen AI Through Lemonade Server

How an OpenAI-compatible local server turns the existing ecosystem of LLM applications - chat UIs, coding assistants, automation tools - into NPU-accelerated local apps on Ryzen AI, without code changes.

Why do companies Open Source Software?

Seven distinct motivations that drive companies to open-source software - from commoditizing complements to building hiring pipelines - and how to read a corporate open-source move for what it actually is.

GraphCast: A breakthrough in Weather Forecasting

How DeepMind's GraphCast uses graph neural networks to produce 10-day global weather forecasts that outperform traditional numerical weather prediction - and what it signals about ML displacing physics-based simulation.

BTS: The Marketing Masters in Disguise

What the world's biggest band can teach product builders about community, distribution, and brand loyalty - a look at BTS's playbook through a marketer's lens.

The Power of Product Ecosystems

Why platforms win through ecosystems rather than individual products: a look at how developer tools, integrations, and community compound - with lessons for ML hardware companies building software moats around silicon.

OpenAI's ChatGPT introduces itself

Written in the week ChatGPT launched: an early hands-on exploration of what conversational LLMs could do, letting the model describe its own capabilities and limitations.

What, Why and How ONNX Script?

ONNX Script lets developers author ONNX functions and models directly in Python with eager-mode evaluation - dramatically improving debuggability over hand-writing graph protos. What it is, why it matters for the ONNX ecosystem, and how to use it.

Developer Velocity You Can't Unsee: Introducing GroqFlow

The launch post for GroqFlow: why compiling a model to a novel AI accelerator should be one function call, not a week of toolchain wrangling - and how GroqFlow turns PyTorch and ONNX models into Groq LPU executables automatically.

The Promise of ONNX

Why an open interchange format matters when every ML framework has its own graph representation: ONNX as the interoperability layer that lets models move between training frameworks, inference runtimes, and hardware accelerators.

Optimizing BERT for Intel CPUs using ONNX Runtime default execution provider

How to accelerate BERT transformer inference on Intel CPU cores using ONNX Runtime's default execution provider - covering graph optimizations, quantization, and the practical export path from framework model to optimized ONNX, published in collaboration with Microsoft's ONNX Runtime team.