Table of Contents:
- Introduction
1.1 Overview of MLPerf Benchmark
1.2 Emergence of Generative AI in MLPerf
1.3 Significance of Large Language Models (LLM) and Stable Diffusion - Key Players and Performance Showcase
2.1 Nvidia’s Dominance in MLPerf Benchmarks
2.1.1 Eos: The 10,752-GPU Supercomputer
2.1.2 Microsoft’s Azure in the Competition
2.2 The Staggering Performance Metrics
2.2.1 Eos’s Rapid Completion of GPT-3 Training
2.2.2 GPU Aggregate Power and Interconnect Speeds - Efficient Scaling with Eos
3.1 Design Innovations in Eos
3.1.1 Tripling H100 GPUs for Enhanced Performance
3.1.2 Implications for Efficient Scaling in Generative AI
3.2 Benchmark Focus: GPT-3 Training Checkpoint
3.2.1 Accessibility Considerations for Wider Industry Adoption
3.2.2 Extrapolated Training Times for Different System Sizes - Intel’s Strategic Moves
4.1 Gaudi 2 Accelerator Chip
4.1.1 Activation of 8-bit Floating Point (FP8) Capabilities
4.1.2 Surpassing Projected Performance Gains
4.2 Price-Performance Advantage
4.2.1 Eitan Medina’s Perspective on Gaudi 2 vs. H100
4.2.2 Anticipating Further Advantages with Gaudi 3 - Beyond MLPerf: Intel’s CPU-Only Systems
5.1 Results and Training Times for CPU-Only Systems
5.1.1 Demonstrating Intel’s Multifaceted Approach to Machine Learning
5.1.2 Implications for the Broader AI Computing Landscape - Conclusion
6.1 Recap of MLPerf Benchmark Highlights
6.2 Industry Trends and Future Developments in Generative AI and Machine Learning
6.3 The Continuous Evolution of AI Computing: Looking Ahead
In a dynamic showcase of the rapid evolution in the field of generative AI, the MLPerf benchmark has once again raised the bar. This benchmark, recognized as the leading public test for assessing computer systems’ prowess in training machine learning neural networks, has recently welcomed the era of generative AI.
This year’s additions include a test for training large language models (LLM), notably GPT-3, and a text-to-image generator called Stable Diffusion.
Key Players and Staggering Performance
The latest benchmark featured computing giants Intel, Nvidia, and Google in a head-to-head competition. Nvidia, with its colossal 10,752-GPU supercomputer named Eos, continued to dominate MLPerf benchmarks. Eos completed the GPT-3 training benchmark in under four minutes, showcasing the incredible capabilities of its GPUs, which boast an aggregate 42.6 exaflops. Microsoft’s Azure, with a system of the same scale, followed closely behind Eos, highlighting the fierce competition in the AI computing landscape.
Efficient Scaling with Eos
Eos’s groundbreaking performance is attributed to its innovative design, tripling the number of H100 GPUs bound into a single machine. This three-fold increase resulted in a 2.8-fold performance improvement, emphasizing the importance of efficient scaling for the continuous enhancement of generative AI. The benchmark tackled by Eos focused on reaching a specific checkpoint in GPT-3 training, making it more accessible for a wider range of companies. Despite Eos’s remarkable speed, the extrapolation suggests that a more reasonably-sized computer would still require four months to complete the same training.
Intel’s Strategic Moves
Intel, not to be outdone, made significant strides in the benchmark with systems utilizing the Gaudi 2 accelerator chip and those relying solely on its 4th generation Xeon CPU. A noteworthy development was the activation of Gaudi 2’s 8-bit floating point (FP8) capabilities, resulting in a remarkable 103 percent reduction in time-to-train for a 384-accelerator cluster. Intel’s commitment to lower precision numbers, like FP8, aligns with industry trends, showcasing a 90 percent gain as projected.
The Price-Performance Advantage
Intel’s Eitan Medina emphasizes the cost-effectiveness of Gaudi 2 compared to Nvidia’s H100, citing a significantly lower price-to-performance ratio. With the upcoming Gaudi 3 accelerator chip, set to enter volume production in 2024, Intel anticipates further strengthening its position in the market. Gaudi 3 will be manufactured using the same semiconductor process as Nvidia’s H100, promising a heightened competitive edge.
Beyond MLPerf: Intel’s CPU-Only Systems
Intel not only showcased its prowess in accelerator-based systems but also submitted results for CPU-only systems. The data revealed impressive training times for various benchmarks, further solidifying Intel’s multifaceted approach to machine learning.
Conclusion
The latest MLPerf benchmark unveils a thriving landscape of competition and innovation in the realm of generative AI. Nvidia’s Eos stands as a testament to the industry’s relentless pursuit of performance, while Intel strategically positions itself for future dominance with advancements in accelerator technology and a focus on cost-effective solutions. As the benchmark results continue to scale, the future promises even more astonishing developments in the field of machine learning and AI computing.