TL;DR:
- MLCommons, an industry group, is creating performance benchmarks for laptops, desktops, and workstations to assess on-device AI capabilities.
- The MLPerf Client working group aims to establish AI benchmarks for consumer PCs running various operating systems, emphasizing real-world use cases.
- The initial benchmark will focus on text-generating models, particularly Meta’s Llama 2.
- Notable members of the MLPerf Client working group include AMD, Arm, Asus, Dell, Intel, Lenovo, Microsoft, Nvidia, and Qualcomm.
- Surprisingly, Apple is absent from both the consortium and the benchmarking initiative.
- These benchmarks are expected to influence device-buying decisions as AI becomes ubiquitous in computing.
Main AI News:
In the realm of AI technology, MLCommons has embarked on a mission to establish performance benchmarks that cater to a wide spectrum of client systems, including laptops, desktops, and workstations. As the shift towards on-device AI gains momentum, the need to assess the computational prowess of these devices becomes paramount. Imagine the difference a few seconds could make when generating images with a generative-AI-powered application, versus waiting for several minutes. Time, they say, is indeed money.
MLCommons, a notable industry consortium responsible for shaping AI-related hardware benchmarking standards, has unveiled a groundbreaking initiative. They have introduced the MLPerf Client working group, dedicated to creating AI benchmarks specifically designed for consumer PCs, running various operating systems such as Windows, Linux, and more. What sets these benchmarks apart is their “scenario-driven” approach, which places a strong emphasis on real-world user scenarios and derives insights from community feedback.
The inaugural benchmark set to be released by MLPerf Client will zero in on text-generating models, with a special focus on Meta’s Llama 2. This particular model has already found its place in MLCommons’ benchmarking suites for datacenter hardware. It’s worth noting that Meta has collaborated extensively with Qualcomm and Microsoft to optimize Llama 2 for Windows-based devices, thereby enhancing the AI capabilities of Windows-running systems.
David Kanter, the Executive Director of MLCommons, expressed, “The time is ripe to bring MLPerf to client systems, as AI is becoming an expected part of computing everywhere. We look forward to teaming up with our members to bring the excellence of MLPerf into client systems and drive new capabilities for the broader community.”
Noteworthy members of the MLPerf Client working group include industry giants like AMD, Arm, Asus, Dell, Intel, Lenovo, Microsoft, Nvidia, and Qualcomm. However, it’s worth mentioning that Apple is notably absent from the consortium. Given that Microsoft engineering director Yannis Minadakis co-chairs the MLPerf Client group, Apple’s non-participation may not come as a complete surprise. Consequently, any AI benchmarks generated by MLPerf Client may not be immediately applicable to Apple devices.
Nonetheless, the anticipation surrounding the benchmarks and tools that will emerge from MLPerf Client remains palpable, irrespective of macOS support. With the enduring presence of AI on the horizon, these metrics could potentially play an increasingly pivotal role in the decision-making process when it comes to purchasing computing devices.
In an optimistic scenario, the MLPerf Client benchmarks could mirror the plethora of online PC build comparison tools, offering valuable insights into the AI performance one can expect from a specific machine. With the involvement of Qualcomm and Arm, who have a significant stake in the mobile device ecosystem, there’s even the possibility of these benchmarks expanding to cover phones and tablets in the future. While it’s early days for this initiative, the future certainly holds promise, and we eagerly await further developments.
Conclusion:
The introduction of MLCommons’ AI benchmarks for client systems signifies a critical shift towards evaluating on-device AI performance. With a focus on real-world use cases and industry collaboration, these benchmarks are poised to impact the market, guiding consumers in making informed choices as AI integration becomes increasingly integral to computing.
