George Cozma and Jordan Ranous join Adam and Bryan to discuss AMD's MI300 announcement and its implications to accelerated compute. They highlight the MI300A, which features CPU and GPU chiplets in the same package. The guests also discuss AMD's approach to hardware and software challenges, adoption of liquid cooling, challenges of training large models, and predictions for the future.
AMD's MI300A features 24 cores and 128GB of HBM memory, allowing for shared memory space and efficient computation.
The paradigm shift in accelerated compute with general-purpose CPUs alongside accelerators brings seamless integration and potential for unified systems.
The increasing adoption of open-source software and advancements in programming models pave the way for robust solutions in accelerated compute.
Deep dives
The MI 300 launch: Impressive architecture and engineering
The MI 300A and X, launched by Oxide, feature impressive architecture and engineering. The MI 300A, which is an APU, has 24 cores and 128 gigabytes of HBM memory. The architecture allows for shared memory space and facilitates efficient computation. The thermal analysis and the micro tunneling inside the silicon are noteworthy engineering choices. The MI 300 series is expected to have a significant impact on enterprise computing, improving customer experiences and driving innovation.
The future of accelerated compute
The MI 300A represents a paradigm shift in accelerated compute, with general-purpose CPUs alongside accelerators. This architecture enables seamless integration and the potential for unified systems. Despite current challenges, such as programming models and software stack, the increasing adoption and advancements in open-source software are paving the way for more robust solutions. The future of accelerated compute holds immense possibilities, transforming the enterprise landscape and empowering engineers.
Power considerations and the transformative potential
As accelerated compute gains traction, power consumption and efficiency become crucial considerations. The sheer amount of power required for training large models is significant, and it poses challenges for broader adoption. Power consumption and costs will undoubtedly influence decisions in training models, promoting the need for more energy-efficient solutions. Nonetheless, the transformative potential of accelerated compute holds promise for improved customer experiences, innovative developments, and drive towards more efficient systems.
The future of accelerated computing
The podcast episode discusses the potential future of accelerated computing, particularly focusing on the AMD MI300A and MI300X chips. The MI300A is seen as more suitable for traditional high-performance computing (HPC) workloads, while the MI300X is expected to be popular in the AI realm. The MI300A is well-suited for HPC code from the 70s and 80s that requires high memory bandwidth, while the MI300X offers greater memory capacity for AI tasks and can handle both training and inference workloads.
Deliberate adoption of AI and responsible use of tools
The conversation also touches upon the cautious and deliberate adoption of AI, encouraging enterprises to take their time to prove out AI models using cloud solutions and fake data sets. It is emphasized that developing and implementing AI models can be human capital expensive, requiring extensive time and effort. The discussion acknowledges the mania surrounding AI and the need for responsible use, highlighting that while AI has the potential to be revolutionary and impactful, it also comes with certain risks and potential for misuse. The conversation draws parallels to historical technological advancements and emphasizes the importance of moderate and responsible approaches to harnessing the power of AI.
George Cozma from Chips and Cheese and Jordan Ranous from Storage Review joined Adam, Bryan, and the Oxide Friends to discuss AMD’s recent MI300 announcement and the implications to accelerated to compute. The MI300A particularly caught our eye--CPU and GPU chiplets on in the same package! Bryan pronounced ML "the biggest thing since the spreadsheet!"... we'll see!
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