You can try out the DeepSeek R1 distilled models on your AMD Ryzen™ AI processor or Radeon™ graphics card in just a few short steps!

AMD, in collaboration with the ONNX Community and the Linux Foundation, has released an open-source tool to help developers explore and understand machine learning models. Digest is a powerful model analysis tool designed to help you extract valuable insights from your machine learning models. With Digest, you can easily organize, report on, and optimize your models, making your workflow more efficient and effective.  

2024 was a transformative year for AMD Instinct™ accelerators. In the last 12 months we successfully delivered dozens of Instinct MI300X platforms to market across our cloud and OEM partners. We ramped numerous customers into volume production, including

AMD is excited to release one-step diffusion models that demonstrate the readiness of AMD Instinct™ MI250 accelerators for model training and further research. The models are designed to provide performance comparable to traditional full-step diffusion models while maintaining the efficiency needed for training on data center systems or deployment on edge devices, such as AI-enabled PCs and laptops.

 Full technical blog AMD Nitro Diffusion: One-Step Text-to-Image Generation Models

ROCm 6.3 marks a significant milestone for the AMD open-source platform, introducing advanced tools and optimizations to elevate AI, ML, and HPC workloads on AMD Instinct GPU accelerators. ROCm 6.3 is engineered to empower a wide range of customers—from innovative AI startups to HPC-driven industries—by enhancing developer productivity

This blog delves into the standout features of this release, including seamless SGLang integration for accelerated AI inferencing, a re-engineered FlashAttention-2 for optimized AI training and inference, the introduction of multi-node Fast Fourier Transform (FFT) to revolutionize HPC workflows and more. Explore these exciting updates and more as ROCm 6.3 continues to drive innovation across industries.

Amuse 2.2 Beta is now available for download. 

In recent years, the rapid development of artificial intelligence technology, especially the progress in large language models (LLMs), has garnered significant attention and discussion. From the emergence of ChatGPT to subsequent models like GPT-4 and Llama, these language models have demonstrated remarkable capabilities in natural language processing, generation, understanding and reasoning. Continuing AMD tradition of open-sourcing models and code to help the community advance together, we are excited to release our first series of fully open 1 billion parameter language models, AMD OLMo.

To dive deeper into the three stages of training, and AMD OLMo model results, please reference the full article here: Introducing the First AMD 1B Language Models: AMD OLMo

See how AMD Ryzen™ AI 300 series-based PCs compare to the competition in llama.cpp based applications like LM Studio. 

In the ever-evolving landscape of artificial intelligence, large language models (LLMs) like GPT-4 and Llama have garnered significant attention for their impressive capabilities in natural language processing and generation. However, small language models (SLMs) are emerging as an essential counterpart in the AI model community offering a unique advantage for specific use cases.  AMD is excited to release its very first small language model, AMD-135M with Speculative Decoding.  This work demonstrates the commitment to an open approach to AI which will lead to more inclusive, ethical, and innovative technological progress, helping ensure that its benefits are more widely shared, and its challenges more collaboratively addressed. 

AMD is advancing generative AI innovation in collaboration with Meta, and its latest Llama 3.2 release, helping ensure developers are well-equipped to handle each new release seamlessly with Day-0 support across our broad AI portfolio.