Group for artificial intelligence According to MLCommons, the two new benchmarks it announced may be used to gauge how fast the best hardware and software can execute AI applications.
Chip manufacturers have started to concentrate on creating hardware that can effectively execute the code that enables millions of people to utilize AI products since the release of OpenAI’s ChatGPT more than two years ago. MLCommons created two updated versions of its MLPerf benchmarks to measure speed since the underlying models that drive AI applications like chatbots and search engines need to answer a lot more questions.
The Llama 3.1 405-billion-parameter AI model developed by Meta provides the basis for one of the new benchmarks. The exam focuses on general question answering, arithmetic, and code production. The new format evaluates a system’s capacity to combine data from several sources and handle complex queries.
Both Nvidia and system makers like Dell Technologies submitted a number of their CPUs for the benchmark.
MLCommons data showed that no Advanced Micro Devices submissions were made for the huge 405-billion-parameter benchmark.
Even when using just eight GPUs in the newer server to directly compare it to the older model, Nvidia’s latest generation of AI servers, called Grace Blackwell, which has 72 GPUs inside, was 2.8 to 3.4 times faster than the previous generation for the new test, the company announced at a briefing on Tuesday.
In AI work, where a chatbot operates on numerous processors simultaneously, Nvidia has been attempting to speed up the connectivity of chips inside its servers.
The goal of the second benchmark is to more accurately mimic the performance requirements established by consumer AI apps like ChatGPT. It is also based on an open-source AI model developed by Meta.
The objective is to reduce the benchmark’s reaction time to nearly instantaneous.
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