0
0
The Significance of MLCommons MLPerf 4.0 Inference Benchmarks
MLCommons has recently released its MLPerf 4.0 benchmarks for inference, shedding light on the rapid advancements in software and hardware within the realm of artificial intelligence. As the field of generative AI continues to expand, a demand arises for impartial and standardized performance benchmarks, a need which MLCommons aims to address through its suite of MLPerf benchmarks.
Updates and Improvements
The latest MLPerf 4.0 Inference results mark a significant development, being the first update on inference benchmarks since the release of the MLPerf 3.1 results in September 2023. Notably, major players in the hardware industry such as Nvidia and Intel have made substantial progress in enhancing both hardware and software to optimize inference performance.
Moreover, the new MLPerf 4.0 benchmark introduces alterations in the benchmarks themselves. While the MLPerf 3.1 benchmark featured large language models like GPT-J 6B for text summarization, the MLPerf 4.0 benchmark now includes the evaluation of the Llama 2 70 billion parameter open model for question and answer (Q&A) tasks, signifying a shift towards more diverse evaluation criteria. Additionally, gen AI image generation has been incorporated for the first time with Stable Diffusion.
Importance of AI Benchmarks
MLPerf benchmarks play a pivotal role in the industry as they offer a standardized platform for evaluating performance across various hardware, software, and AI use cases, with more than 8,500 performance results in the latest release. According to David Kanter, the Founder, and Executive Director of MLCommons, these benchmarks are essential for improving the speed, efficiency, and accuracy of AI systems, helping to guide buyers in making informed decisions.
One of the key objectives of MLCommons is to align the industry by ensuring that all benchmark results are conducted under similar conditions, enabling enterprises to compare different systems effectively. With the standardized approach facilitated by MLPerf benchmarks, organizations can make optimal choices when selecting systems for tasks like large language model inference.
Nvidia’s Performance Advancements
Nvidia, a dominant force in the MLPerf benchmarks, has once again showcased remarkable performance improvements with its existing hardware through the utilization of TensorRT-LLM open-source inference technology. Notable achievements include nearly tripling the inference performance for text summarization using the GPT-J LLM on the H100 Hopper GPU in a brief period of six months.
Nvidia’s recent introduction of the Blackwell GPU, succeeding the Hopper architecture, further underscores the company’s commitment to pushing the boundaries of AI performance. The MLPerf 4.0 results also unveil the enhanced capabilities of the H200 GPU, showcasing up to a 45% increase in speed compared to the H100 with the Llama 2 model.
Intel’s Role in AI Inference
Intel, another key participant in the MLPerf 4.0 benchmarks, has made significant strides with its Habana AI accelerator and Xeon CPU technologies. While Intel’s Gaudi performance lags behind Nvidia’s H100, the company emphasizes a superior price-performance ratio. Noteworthy advancements come from the 5th Gen Intel Xeon processor, showcasing up to 1.42 times faster performance than its predecessor in various MLPerf categories.
Intel’s focus on CPUs for inference demonstrates the company’s recognition of the importance of a mixed general purpose and AI environment in enterprise AI solutions. By leveraging the capabilities of the 5th Gen Intel Xeon and its AMX engine, Intel aims to provide a balanced approach for customers deploying AI technologies.
Image/Photo credit: source url
