MLCommons introduces Latest MLPerf Inference v5.0 Benchmark Findings

MLCommons introduces Exciting MLPerf Inference v5.0 Results

Today, MLCommons dropped some intriguing news: they’ve released the latest results for their industry-standard MLPerf Inference v5.0 benchmark suite. This suite is all about measuring the performance of machine learning (ML) systems in a way that’s fair, reproducible, and architecture-neutral. And guess what? The spotlight is firmly on generative AI scenarios, which have seen a surge in interest and innovation lately.

With recent advancements in both hardware and software specifically tailored for generative AI, we’re witnessing some jaw-dropping performance improvements compared to last year. The MLPerf Inference benchmark suite evaluates how swiftly systems can execute AI and ML models across a variety of workloads, covering both datacenter and edge systems. Think of it as a level playing field that encourages competition, drives innovation, and boosts energy efficiency across the board. Plus, it’s a goldmine of technical insights for anyone looking to buy or fine-tune AI systems.

Generative AI Takes Center Stage

One of the standout features of the Inference v5.0 results is the incredible momentum behind generative AI. Submissions to the Llama 2 70B benchmark test—a significant generative AI workload—have skyrocketed by 2.5 times in just a year. This test, based on a highly regarded open-source model, has now overtaken Resnet50 as the benchmark with the highest submission rate. And the performance metrics? They’re nothing short of impressive. The median submitted score has doubled, while the top score is a staggering 3.3 times faster than what we saw in Inference v4.0.

“It’s clear now that much of the ecosystem is focused squarely on deploying generative AI, and that the performance benchmarking feedback loop is working,” said David Kanter, head of MLPerf at MLCommons. He points out that we’re seeing an “unprecedented flood of new generations of accelerators,” all of which are paired with innovative software techniques. This combination is setting new records for generative AI inference performance.

New Benchmarks to Watch

The latest round of benchmark results also introduces four new tests, including the Llama 3.1 405B, Llama 2 70B Interactive, RGAT, and Automotive PointPainting. The Llama 3.1 405B model is a game changer, boasting an impressive 405 billion parameters and supporting input/output lengths of up to 128,000 tokens. That’s a massive leap from the 4,096 tokens supported by Llama 2 70B! This benchmark tests three separate tasks: general question-answering, math, and code generation.

“This is our most ambitious inference benchmark to date,” said Miro Hodak, co-chair of the MLPerf Inference working group. He emphasizes that as models grow larger, they can handle more complex tasks and improve accuracy. While these tests may be more challenging and time-consuming, they reflect the industry’s shift toward deploying real-world models of this scale.

Additionally, the Inference v5.0 suite now includes a low-Latency version of the Llama 2 70B test—aptly named Llama 2 70B Interactive. This update reflects the growing trend toward interactive chatbots and advanced reasoning systems, requiring systems under test to meet stricter Response Time metrics.

New Benchmarks for a New Era

In a move that aligns with the fast-paced evolution of AI, MLPerf Inference v5.0 also introduces a new datacenter benchmark based on a graph neural network (GNN) model. GNNs are essential for modeling relationships within networks and are widely used in applications like recommendation systems and fraud detection. The new RGAT benchmark is based on an extensive dataset with over 547 million nodes and nearly 5.8 billion edges.

On the edge computing front, there’s an exciting addition: the Automotive PointPainting benchmark, designed for 3D object detection in camera feeds, particularly for self-driving cars. This test is a crucial step in developing AI systems that can navigate real-world scenarios.

Raising the Bar for AI Systems

It’s not every day that we see four new tests rolled out in a single update, but Miro Hodak stresses the necessity of this move in light of the rapid advancements in machine learning. With 17,457 performance results submitted by 23 organizations, including industry heavyweights like AMD, Intel, and nVidia, the breadth of this release speaks volumes about the growing community and the importance of accurate performance metrics.

“We’d like to extend a warm welcome to our five first-time submitters,” Kanter added, recognizing the contributions of newcomers like CoreWeave and FlexAI. Their participation underscores the significance of reliable performance data in the AI landscape.

As we look ahead, it’s clear that the machine learning ecosystem is expanding, with new capabilities emerging at an unprecedented rate. From larger AI models to enhanced responsiveness and broader deployment, the landscape is shifting. And with MLCommons leading the charge, we can expect even more exciting developments in the MLPerf Inference benchmark suite.

Check Out the Results

Curious to see how these benchmarks stack up? You can dive into the MLPerf Inference v5.0 results by visiting the Datacenter and Edge benchmark results pages. You won’t want to miss the insights and data that could shape the future of AI systems!

Background Information

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About AMD:

AMD, a large player in the semiconductor industry is known for its powerful processors and graphic solutions, AMD has consistently pushed the boundaries of performance, efficiency, and user experience. With a customer-centric approach, the company has cultivated a reputation for delivering high-performance solutions that cater to the needs of gamers, professionals, and general users. AMD's Ryzen series of processors have redefined the landscape of desktop and laptop computing, offering impressive multi-core performance and competitive pricing that has challenged the dominance of its competitors. Complementing its processor expertise, AMD's Radeon graphics cards have also earned accolades for their efficiency and exceptional graphical capabilities, making them a favored choice among gamers and content creators. The company's commitment to innovation and technology continues to shape the client computing landscape, providing users with powerful tools to fuel their digital endeavors.

  

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About Intel:

Intel Corporation, a global technology leader, is for its semiconductor innovations that power computing and communication devices worldwide. As a pioneer in microprocessor technology, Intel has left an indelible mark on the evolution of computing with its processors that drive everything from PCs to data centers and beyond. With a history of advancements, Intel's relentless pursuit of innovation continues to shape the digital landscape, offering solutions that empower businesses and individuals to achieve new levels of productivity and connectivity.

  

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About nVidia:

NVIDIA has firmly established itself as a leader in the realm of client computing, continuously pushing the boundaries of innovation in graphics and AI technologies. With a deep commitment to enhancing user experiences, NVIDIA's client computing business focuses on delivering solutions that power everything from gaming and creative workloads to enterprise applications. for its GeForce graphics cards, the company has redefined high-performance gaming, setting industry standards for realistic visuals, fluid frame rates, and immersive experiences. Complementing its gaming expertise, NVIDIA's Quadro and NVIDIA RTX graphics cards cater to professionals in design, content creation, and scientific fields, enabling real-time ray tracing and AI-driven workflows that elevate productivity and creativity to unprecedented heights. By seamlessly integrating graphics, AI, and software, NVIDIA continues to shape the landscape of client computing, fostering innovation and immersive interactions in a rapidly evolving digital world.

  

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Technology Explained

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Latency: Technology latency is the time it takes for a computer system to respond to a request. It is an important factor in the performance of computer systems, as it affects the speed and efficiency of data processing. In the computer industry, latency is a major factor in the performance of computer networks, storage systems, and other computer systems. Low latency is essential for applications that require fast response times, such as online gaming, streaming media, and real-time data processing. High latency can cause delays in data processing, resulting in slow response times and poor performance. To reduce latency, computer systems use various techniques such as caching, load balancing, and parallel processing. By reducing latency, computer systems can provide faster response times and improved performance.

Latest Articles about Latency

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Response Time: Technology response time is the amount of time it takes for a computer system to respond to a given input from a user. It is a measure of how quickly a computer system can respond to a user’s query or instruction. The lower the response time the better the performance of the system. The response time is an important factor in the performance of different computer systems. In the computer industry, technology response time can be used to measure the efficiency of different computers, meaning that the faster the response time the higher the performance. Technology response time can also be used to compare the performance of different computer systems by measuring how quickly they can respond to a given input.

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