Featured Releases

Instella-Math

First Math LLM with Long CoT RL on AMD GPUs

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Instella-T2I

Pushing the Limits of 1D Discrete Latent Space Image Generation

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Instella-Long

A Fully Open Language Model with Long-Context

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AMD GenAI advances the frontier of AI through open innovation and collaboration. We build fully open-source foundation models optimized for AMD GPUs such as MI250 and MI300, and release everything -- code, models, and training recipes -- to the public.

Our mission is to empower AI researchers and engineers to train and develop cutting-edge models efficiently on AMD systems.

AMD GenAI is part of AMD. To learn more about AMD, visit AMD.

Recent Blogs
SAND-Math: Building a 32B Reasoning Model with Synthetic Data
December 06, 2025
Building a state-of-the-art 32B reasoning model on AMD Instinct™ MI325X GPUs using only synthetic data. SAND-MathScience-DeepSeek-Qwen32B achieves 78.33% on AIME25, surpassing Qwen3-32B, with fully open-sourced model weights, datasets, and training code.
slime: SGLang-Native RL Framework on AMD GPUs
September 25, 2025
AMD provides Day-0 support for slime, the SGLang-native RL framework verified for large-scale MoE models up to 355B parameters. Features APRIL optimization achieving 40% throughput improvement, Docker deployment, and multi-turn kernel-agent training on AMD Instinct™ GPUs.
Instella-Math
August 9, 2025
Introducing Instella-Math — AMD's first fully open reasoning language model, trained end-to-end with long chain-of-thought reinforcement learning on 32 MI300X GPUs. Optimized for complex reasoning and math, with all code, weights, and datasets released for the community.
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Recent Publications
CaptionQA: Is Your Caption as Useful as the Image Itself?
Publication
2025
CaptionQA: Is Your Caption as Useful as the Image Itself?
CaptionQA is a utility-based benchmark evaluating caption quality across 4 domains with 33,027 annotated questions, revealing up to 32% gap between image and caption utility in state-of-the-art MLLMs.
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APRIL: Active Partial Rollouts in Reinforcement Learning to Tame Long-tail Generation
Publication
2025
APRIL: Active Partial Rollouts in Reinforcement Learning to Tame Long-tail Generation
APRIL mitigates long-tail inefficiency in RL training by over-provisioning rollout requests, recycling incomplete responses, and reducing GPU idle time — achieving up to 44% throughput improvement and 8% higher accuracy across GRPO, DAPO, and GSPO algorithms.
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SAND-Math: Using LLMs to Generate Novel, Difficult and Useful Mathematics Questions and Answers
Publication
NeurIPS 2025 MATH-AI Workshop
SAND-Math: Using LLMs to Generate Novel, Difficult and Useful Mathematics Questions and Answers
SAND-Math, a scalable pipeline that generates and enhances challenging math problems, enabling LLMs to achieve state-of-the-art results on difficult mathematical reasoning benchmarks like AIME25.
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