AMD has released a version 6.2 of its ROCm software stack for GPU programming. Global AI GPU Product Marketing Manager Ronak Shah wrote a blog in support of the announcement:
Whether you’re working on cutting-edge AI models, developing next-gen AI applications, or optimizing complex simulations, this new release brings amazing performance, efficiency, and scalability enhancements. In this blog, we’ll dive into the top 5 key enhancements that make this release transformative, solidifying position of AMD ROCm as one of the leading platforms for AI & HPC development.
1. Extending vLLM Support in ROCm 6.2 – Advancing AI Inference Capabilities of AMD Instinct Accelerators
AMD is expanding vLLM support to enhance the efficiency and scalability of AI models on AMD Instinct Accelerators. Designed for Large Language Models (LLMs), vLLM addresses key inferencing challenges such as efficient multi-GPU computation, reduced memory usage and minimized computational bottlenecks. Customers can enable various upstream vLLM features like multi-GPU execution and FP8 KV cache to tackle these challenges by following the steps provided in the ROCm documentation here. To access cutting-edge performance features, the ROCm/vLLM branch offers advanced experimental capabilities such as FP8 GEMMs and custom decode paged attention. To utilize these features, follow the steps provided here and select the rocm/vllm branch when cloning the git repository. Alternatively, these features are also available through a dedicated Docker file.
With the ROCm 6.2 release, existing and new AMD Instinct™ customers can confidently integrate vLLM into their AI pipelines, benefiting from the latest features for improved performance and efficiency.
The rest of the blog can be found here.
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