Case Study: AI accelerators NVIDIA
AI accelerators NVIDIA Case Study
NVIDIA AI Processors: Performance Metrics
NVIDIA is a leading manufacturer of graphics processing units (GPUs) and specialized AI accelerators. Their flagship AI processors are the Tensor Core GPUs and the recently announced H100 GPU, designed for high-performance computing (HPC) and AI workloads.
1. TFLOPS (Tera Floating-Point Operations Per Second)
NVIDIA's AI processors deliver exceptional floating-point performance measured in TFLOPS. The A100 GPU, for instance, offers up to 312 TFLOPS of FP16 tensor core performance, while the more recent H100 GPU boasts up to 500 TFLOPS of FP16 tensor core performance.
2. TOPS (Tera Operations Per Second)
In addition to TFLOPS, NVIDIA also reports TOPS figures for their AI processors, which measure overall computational throughput, including both floating-point and integer operations. The A100 GPU delivers up to 19.5 TOPS of INT8 performance, while the H100 GPU offers up to 60 TOPS of INT8 performance.
3. Watts per TFLOP/TOPS (Energy Efficiency)
Energy efficiency is a crucial factor for AI accelerators, especially in data centers and cloud environments. NVIDIA has made significant strides in improving the performance-per-watt of their AI processors. The A100 GPU delivers up to 20 TFLOPS per watt (FP16) and up to 1.25 TOPS per watt (INT8). The H100 GPU further improves on this, offering up to 30 TFLOPS per watt (FP16) and up to 1.8 TOPS per watt (INT8).
4. Latency (Time to process a single input)
Low latency is essential for real-time AI applications, such as natural language processing, recommendation systems, and autonomous vehicles. NVIDIA's AI processors are designed to provide low-latency inference capabilities. For example, the A100 GPU can process a single input in as little as 2.8 milliseconds for computer vision workloads, while the H100 GPU promises even lower latency, although specific figures are not yet available.
5. Throughput (Number of inputs processed per second)
Throughput is a critical metric for batch processing and high-volume AI workloads, such as training large language models or processing video streams. NVIDIA's AI processors excel in this area, with the A100 GPU capable of processing up to 1.6 billion images per second for computer vision tasks. The H100 GPU is expected to deliver even higher throughput, with NVIDIA claiming up to 3.5 times higher performance compared to the A100 for certain AI workloads.
In summary, NVIDIA's AI processors, including the A100 and H100 GPUs, offer exceptional performance across TFLOPS, TOPS, energy efficiency, latency, and throughput metrics. These capabilities make NVIDIA's AI accelerators well-suited for a wide range of AI applications, from high-performance training to low-latency inference tasks.
