The MLPerf LLM inference benchmark is quite different from the training benchmark, he emphasized. Summarization is what we do here,” said Kanter. You can use them in search and in generating content, like essays or summaries. In practice, LLM s are used in a wide variety of applications. Now, you can chain this together to actually build a predicted sentence. An LLM simply takes a set of tokens as input and predicts the next token. “It’s important to understand large language models operate on tokens. No ML benchmarking effort today would be complete without LLM coverage and MLCommon (parent organization for MLPerf) now has that. The LLM (inference) benchmark is brand new and reflects the explosion of interest in what people are calling generative AI, large language models.” An LLM had been added to the MLPerf Training benchmark in the spring (see HPCwire coverage, MLPerf Training 3.0 Showcases LLM Nvidia Dominates, Intel/Habana Also Impress) In a pre-briefing, David Kanter, MLCommons executive director, said, “We added our first generation recommender a couple of years ago and are now updating it. These new tests, say MLCommons, help advance AI by ensuring that industry-standard benchmarks represent the latest trends in AI adoption to help guide customers, vendors, and researchers, says MLCommons. The second change is an updated recommender, meant be more representative of industry practices, using the DLRM-DCNv2 reference model and larger datasets it had 9 submissions.
The first is a large language model (LLM) using the GPT-J reference model to summarize CNN news articles it garnered results from 15 different submitters, reflecting the rapid adoption of generative AI. MLPerf Inference v3.1 introduced two new benchmarks to the suite. Inferencing, though, is the volume workhorse, sitting behind every chatbot and similar applications. think of training LLMs with trillions of parameters). The HPC Training benchmark is released just once yearly, close to the annual SC conference.īroadly, inferencing and training are the foundational pieces of ML applications, with training deemed the more computational-intense of the two (i.e. – so, inference results are released in spring and (early) fall training results are released in winter and summer. By now, you may be familiar with the MLPerf release cadence which is twice yearly for training and inference, with each released on alternate quarters. Instead, we’ll present a broad overview in this article and drill deeper into some vendor-specific results in separate articles (Nvidia and Intel/Habana). The rising number of results and introduction of a new category make this less tenable.
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In the past, HPCwire has tended to try to cover the full exercise in a single article. (Links to Inference Datacenter and Edge v3.1 results and Storage v0.5 results) From a usefulness perspective, MLCommons provides direct access to results spreadsheets that permit potential system users/buyers to drill down onto specific system configurations and benchmark tests for comparison. There were a more modest 28 results in the storage category. Submitters in the first Storage run included: Argonne National Laboratory (ANL), DDN, Micron, Nutanix, and Weka.ĭigging through the latest Inference results – more than 12,000 performance and 5,000 power inferencing results from 120 systems – is a challenge. MLCommons also debuted a new MLPerf Storage (v0.5) benchmark intended to measure storage performance under ML training workloads. Google provided a peak at its new TPU (v5e) performance. Nvidia was again the top performing accelerator, but Intel (Xeon CPU) and Habana (Gaudi1 and 2) performed well.
MLCommons this week issued the results of its latest MLPerf Inference (v3.1) benchmark exercise. Since 1987 - Covering the Fastest Computers in the World and the People Who Run Them
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