Originally posted on Datalec
When it comes to running the advanced systems behind artificial intelligence (AI), Graphics Processing Units (GPUs) are the unsung heroes. Their unique design makes them perfect for handling the intense demands of AI workloads, far outpacing traditional computer processors in these tasks.
Why GPU’s, not CPU’s?
CPUs (Central Processing Units) and GPUs (Graphics Processing Units) are designed for different types of computing tasks. CPUs have fewer, high-performance cores optimised for sequential operations, handling tasks such as system management, logic-based processes, and decision-making. In contrast, GPUs consist of thousands of lightweight, efficient cores built for parallel processing, making them ideal for tasks like rendering graphics and AI training.
The NVIDIA H200 Tensor Core GPU exemplifies advanced GPU architecture. It boasts 141 GB of HBM3e memory with a massive bandwidth of 4.8 TB/s, delivering up to 3,958 TFLOPS for FP8 computations. This high-performance design makes it suitable for intensive data-driven applications, AI models, and scientific computing. The architecture prioritises raw computational power by dedicating transistors to arithmetic units rather than control logic and cache, as seen in CPUs.
In comparison, AMD’s Instinct MI325X GPU, based on the CDNA™ 3 architecture, offers 256 GB of HBM3e memory with a bandwidth of 6.0 TB/s. It delivers up to 2.6 PFLOPS for FP8 operations and 1.3 PFLOPS for FP16 operations. AMD claims that the MI325X outperforms the NVIDIA H200 in certain AI inference workloads*, providing up to 40% faster throughput with a 7-billion-parameter Mixtral model and 20% lower latency with a 70-billion-parameter Llama 3.1 model.
These specifications highlight the GPUs’ emphasis on high-throughput parallel processing, contrasting with CPUs’ design for versatile, sequential task handling. The choice between these GPUs depends on specific workload requirements, including memory capacity, bandwidth, and performance in AI inference tasks.
Built for Big Jobs
GPUs are designed to handle many tasks at once, thanks to thousands of small processing units working together. This makes them ideal for performing the kinds of calculations AI relies on, like the ones needed for recognising patterns, making predictions, or even training complex models.
Special Features for AI
Modern GPUs include specialised components that give them an extra boost for AI work. These features allow them to handle AI-related calculations faster and more efficiently, speeding up tasks like training models and making predictions. This means less waiting time and faster results for everything from chatbots to advanced medical imaging systems.
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