https://www.youtube.com/watch?v=BRUWmryoztQ
H100 GPUs set new records on all eight tests in the latest MLPerf training benchmarks released today, excelling on a new MLPerf test for generative AI. That

https://developer.nvidia.com/blog/breaking-mlperf-training-records-with-nvidia-h100-gpus/
ResNet-50 v1.5. In MLPerf Training v3.0, NVIDIA and CoreWeave made submissions using up to 3,584 H100 Tensor Core GPUs, setting a new at-scale record of 0.183 minutes (just under 11 seconds). Additionally, H100 per-accelerator performance improved by 8.4% compared to the prior submission through software improvements.

https://blogs.nvidia.com/blog/generative-ai-debut-mlperf/
H100 GPUs set new records on all eight tests in the latest MLPerf training benchmarks released today, excelling on a new MLPerf test for generative AI. That excellence is delivered both per-accelerator and at-scale in massive servers. For example, on a commercially available cluster of 3,584 H100 GPUs co-developed by startup Inflection AI and

https://blogs.nvidia.com/blog/scaling-ai-training-mlperf/
The latest results were due in part to the use of the most accelerators ever applied to an MLPerf benchmark. The 10,752 H100 GPUs far surpassed the scaling in AI training in June, when NVIDIA used 3,584 Hopper GPUs. The 3x scaling in GPU numbers delivered a 2.8x scaling in performance, a 93% efficiency rate thanks in part to software optimizations.

https://www.forbes.com/sites/stevemcdowell/2023/06/27/nvidia-h100-dominates-new-mlperf-v30-benchmark-results/
A large-scale AI system built by NVIDIA & Inflection AI, hosted by CoreWeave, uses a large number of NVIDIA H100 GPUs to train GPT-3 in 11 minutes. One of many records.

https://www.techpowerup.com/310592/nvidia-h100-gpus-set-standard-for-generative-ai-in-debut-mlperf-benchmark
H100 GPUs set new records on all eight tests in the latest MLPerf training benchmarks released today, excelling on a new MLPerf test for generative AI. That excellence is delivered both per-accelerator and at-scale in massive servers. For example, on a commercially available cluster of 3,584 H100 GPUs co-developed by startup Inflection AI and

https://spectrum.ieee.org/large-language-models-training-benchmark
By one estimate, Nvidia and CoreWeave's 11-minute record-setting training time would scale up to about two days of full-scale training. Computer scientists have found that for GPT-3's type of

https://www.jonpeddie.com/news/nvidia-h100-gpus-set-time-to-beat-in-mlperf-generative-ai-benchmark-debut/
Nvidia's H100 Tensor Core GPUs have gained recognition for their AI performance, particularly in large language models (LLMs) that power generative AI. ... completed a GPT-3-based training benchmark in under 11 minutes. Inflection AI used the H100 GPUs to create an advanced LLM for its personal AI assistant, Pi. ... The company said it is

https://dailyai.com/2023/06/nvidias-h100-chips-smash-ai-training-and-deployment-records-in-benchmark-test/
Nvidia, the world leader in AI hardware, tested a cluster of 3,584 H100 GPUs to flex their formidable speed. The cluster, co-developed by AI startup Inflection AI and managed by CoreWeave, a cloud service provider specializing in GPU-based workloads, completed a training benchmark based on the GPT-3 model in less than 11 minutes.

https://developer.nvidia.com/blog/full-stack-innovation-fuels-highest-mlperf-inference-2-1-results-for-nvidia/
Figure 1. H100 delivers up to 4.5x more performance than A100 in the MLPerf Inference 2.1 Data Center category. Thanks to full-stack improvements, NVIDIA Jetson AGX Orin turned in large improvements in energy efficiency compared to the last round, delivering up to a 50% efficiency improvement. Figure 2. Efficiency improvements in the NVIDIA

https://www.forbes.com/sites/moorinsights/2022/11/21/nvidia-h100-gpu-performance-shatters-machine-learning-benchmarks-for-model-training/
The H100 set world records in all of them and NVIDIA is the only company to have submitted to every workload for every MLPerf round. ... allowing the H100 to train models within days or hours

https://blogs.nvidia.com/blog/mlperf-ai-training-hpc-hopper/
Two months after their debut sweeping MLPerf inference benchmarks, NVIDIA H100 Tensor Core GPUs set world records across enterprise AI workloads in the industry group's latest tests of AI training.. Together, the results show H100 is the best choice for users who demand utmost performance when creating and deploying advanced AI models.

https://developer.nvidia.com/blog/setting-new-records-at-data-center-scale-using-nvidia-h100-gpus-and-quantum-2-infiniband/
The NVIDIA platform and H100 GPUs submitted record-setting results for the newly added Stable Diffusion workloads. The NVIDIA submission using 64 H100 GPUs completed the benchmark in just 10.02 minutes, and that time to train was reduced to just 2.47 minutes using 1,024 H100 GPUs.

https://moorinsightsstrategy.com/nvidia-h100-gpu-performance-shatters-machine-learning-benchmarks-for-model-training/
The H100 set world records in all of them and NVIDIA is the only company to have submitted to every workload for every MLPerf round. A few weeks ago, a new set of MLCommons training results were released, this time for MLPerf 2.1 Training, which the NVIDIA H100 and A100 also dominated. ... NVIDIA was able to train every workload at scale in

https://www.extremetech.com/computing/nvidias-eos-supercomputer-sets-new-records-in-ai-training-showdown
Its supercomputer can now train a GPT-3 model with 175 billion parameters in under 4 minutes. Nvidia is the current top dog when it comes to AI hardware, and to prove it, the company recently set

https://wccftech.com/nvidia-hopper-ampere-ai-gpus-continue-to-post-world-records-in-ai-training-benchmarks/
NVIDIA H100 GPUs (aka Hopper) set world records for training models in all eight MLPerf enterprise workloads. They delivered up to 6.7x more performance than previous-generation GPUs when they

https://www.enterpriseai.news/2023/11/10/nvidia-enhances-ai-training-efficiency-completes-gpt-3-model-in-under-4-minutes/
That's a nearly 3x gain from 10.9 minutes, the record NVIDIA set when the test was introduced less than six months ago. The benchmark uses a portion of the full GPT-3 data set behind the popular ChatGPT service that, by extrapolation, Eos could now train in just eight days, 73x faster than a prior state-of-the-art system using 512 A100 GPUs.

https://www.reddit.com/r/singularity/comments/14l5unz/nvidia_h100_dominates_new_mlperf_v30_benchmark/
In the benchmark tests, the NVIDIA H100 set records on every workload in the MLPerf training and inference benchmarks. One of the most impressive results was a system developed by NVIDIA and Inflection AI, hosted by CoreWeave, which used a large number of NVIDIA H100 GPUs to train GPT-3 a GPT3 benchmark in just 11 minutes. Alternate source

https://blogs.nvidia.com/blog/hopper-mlperf-inference/
In their debut on the MLPerf industry-standard AI benchmarks, NVIDIA H100 Tensor Core GPUs set world records in inference on all workloads, delivering up to 4.5x more performance than previous-generation GPUs.. The results demonstrate that Hopper is the premium choice for users who demand utmost performance on advanced AI models.

https://www.reddit.com/r/mlscaling/comments/14ktx00/nvidia_h100_cluster_completed_mlperf_gpt3/
NVIDIA H100 cluster completed MLPerf GPT-3 training benchmark in 11 minutes. That's impressive since GPT-3 took 34 days to train. Down to 11 minutes means they were roughly 4,450x faster, unless I missed something.

https://www.engadget.com/nvidias-eos-supercomputer-just-broke-its-own-ai-training-benchmark-record-170042546.html
In all, NVIDIA set six records in nine benchmark tests: the 3.9 minute notch for GPT-3, a 2.5 minute mark to to train a Stable Diffusion model using 1,024 Hopper GPUs, a minute even to train DLRM

https://www.reddit.com/r/singularity/comments/17qy5ml/nvidia_eosan_ai_supercomputer_powered_by_10752/
Nvidia is talking about how fast their new GPUs are able to train AI models. They can now "recreate" GPT3 in 3 minutes, and ChatGPT in 9. They also showed adding more GPUs increased the training speed on a linear basis - that is, adding 3 times more GPUs actually did increase speed by 3 times.

https://www.codetd.com/en/article/15619614
In the latest MLPerf training benchmark test, the H100 GPU set new records in all eight tests! Today, the NVIDIA H100 pretty much dominates all categories and is the only GPU used in the new LLM benchmark. Edit toggle to center. Add picture annotations, no more than 140 words (optional) A cluster of 3,584 H100 GPUs completed a large-scale

https://www.ibm.com/blog/
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