MLPerf – NVIDIA Technical Blog News and tutorials for developers, data scientists, and IT admins 2025-05-16T23:50:38Z http://www.open-lab.net/blog/feed/ Ashraf Eassa <![CDATA[NVIDIA Blackwell Delivers Massive Performance Leaps in MLPerf Inference v5.0]]> http://www.open-lab.net/blog/?p=98367 2025-04-23T19:41:12Z 2025-04-02T18:14:48Z The compute demands for large language model (LLM) inference are growing rapidly, fueled by the combination of growing model sizes, real-time latency...]]> The compute demands for large language model (LLM) inference are growing rapidly, fueled by the combination of growing model sizes, real-time latency...

The compute demands for large language model (LLM) inference are growing rapidly, fueled by the combination of growing model sizes, real-time latency requirements, and, most recently, AI reasoning. At the same time, as AI adoption grows, the ability of an AI factory to serve as many users as possible, all while maintaining good per-user experiences, is key to maximizing the value it generates.

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Sukru Burc Eryilmaz <![CDATA[NVIDIA Blackwell Doubles LLM Training Performance in MLPerf Training v4.1]]> http://www.open-lab.net/blog/?p=91807 2024-11-14T17:10:37Z 2024-11-13T16:00:00Z As models grow larger and are trained on more data, they become more capable, making them more useful. To train these models quickly, more performance,...]]> As models grow larger and are trained on more data, they become more capable, making them more useful. To train these models quickly, more performance,...

As models grow larger and are trained on more data, they become more capable, making them more useful. To train these models quickly, more performance, delivered at data center scale, is required. The NVIDIA Blackwell platform, launched at GTC 2024 and now in full production, integrates seven types of chips: GPU, CPU, DPU, NVLink Switch chip, InfiniBand Switch, and Ethernet Switch.

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Amr Elmeleegy <![CDATA[NVIDIA GH200 Grace Hopper Superchip Delivers Outstanding Performance in MLPerf Inference v4.1]]> http://www.open-lab.net/blog/?p=89401 2024-11-06T02:27:00Z 2024-09-24T16:36:57Z In the latest round of MLPerf Inference �C a suite of standardized, peer-reviewed inference benchmarks �C the NVIDIA platform delivered outstanding...]]> In the latest round of MLPerf Inference �C a suite of standardized, peer-reviewed inference benchmarks �C the NVIDIA platform delivered outstanding...

In the latest round of MLPerf Inference �C a suite of standardized, peer-reviewed inference benchmarks �C the NVIDIA platform delivered outstanding performance across the board. Among the many submissions made using the NVIDIA platform were results using the NVIDIA GH200 Grace Hopper Superchip. GH200 tightly couples an NVIDIA Grace CPU with an NVIDIA Hopper GPU using NVIDIA NVLink-C2C��

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Ashraf Eassa <![CDATA[NVIDIA Blackwell Platform Sets New LLM Inference Records in MLPerf Inference v4.1]]> http://www.open-lab.net/blog/?p=87957 2024-09-05T17:57:17Z 2024-08-28T15:00:00Z Large language model (LLM) inference is a full-stack challenge. Powerful GPUs, high-bandwidth GPU-to-GPU interconnects, efficient acceleration libraries, and a...]]> Large language model (LLM) inference is a full-stack challenge. Powerful GPUs, high-bandwidth GPU-to-GPU interconnects, efficient acceleration libraries, and a...

Large language model (LLM) inference is a full-stack challenge. Powerful GPUs, high-bandwidth GPU-to-GPU interconnects, efficient acceleration libraries, and a highly optimized inference engine are required for high-throughput, low-latency inference. MLPerf Inference v4.1 is the latest version of the popular and widely recognized MLPerf Inference benchmarks, developed by the MLCommons��

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Ashraf Eassa <![CDATA[NVIDIA Sets New Generative AI Performance and Scale Records in MLPerf Training v4.0]]> http://www.open-lab.net/blog/?p=83776 2024-06-27T18:18:05Z 2024-06-12T15:00:00Z Generative AI models have a variety of uses, such as helping write computer code, crafting stories, composing music, generating images, producing videos, and...]]> Generative AI models have a variety of uses, such as helping write computer code, crafting stories, composing music, generating images, producing videos, and...Decorative image of rows of GPUs.

Generative AI models have a variety of uses, such as helping write computer code, crafting stories, composing music, generating images, producing videos, and more. And, as these models continue to grow in size and are trained on even more data, they are producing even higher-quality outputs. Building and deploying these more intelligent models is incredibly compute-intensive��

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Ashraf Eassa <![CDATA[NVIDIA H200 Tensor Core GPUs and NVIDIA TensorRT-LLM Set MLPerf LLM Inference Records]]> http://www.open-lab.net/blog/?p=80197 2024-11-14T15:53:12Z 2024-03-27T15:29:05Z Generative AI is unlocking new computing applications that greatly augment human capability, enabled by continued model innovation. Generative AI...]]> Generative AI is unlocking new computing applications that greatly augment human capability, enabled by continued model innovation. Generative AI...An image of an NVIDIA H200 Tensor Core GPU.

Generative AI is unlocking new computing applications that greatly augment human capability, enabled by continued model innovation. Generative AI models��including large language models (LLMs)��are used for crafting marketing copy, writing computer code, rendering detailed images, composing music, generating videos, and more. The amount of compute required by the latest models is immense and��

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Feiwen Zhu <![CDATA[Optimizing OpenFold Training for Drug Discovery]]> http://www.open-lab.net/blog/?p=78346 2024-03-07T19:18:52Z 2024-02-28T19:29:02Z Predicting 3D protein structures from amino acid sequences has been an important long-standing question in bioinformatics. In recent years, deep...]]> Predicting 3D protein structures from amino acid sequences has been an important long-standing question in bioinformatics. In recent years, deep...Decorative image of colorful protein structures.

Predicting 3D protein structures from amino acid sequences has been an important long-standing question in bioinformatics. In recent years, deep learning�Cbased computational methods have been emerging and have shown promising results. Among these lines of work, AlphaFold2 is the first method that has achieved results comparable to slower physics-based computational methods.

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Ashraf Eassa <![CDATA[Setting New Records at Data Center Scale Using NVIDIA H100 GPUs and NVIDIA Quantum-2 InfiniBand]]> http://www.open-lab.net/blog/?p=72467 2023-11-24T18:36:30Z 2023-11-08T17:00:00Z Generative AI is rapidly transforming computing, unlocking new use cases and turbocharging existing ones. Large language models (LLMs), such as OpenAI��s GPT...]]> Generative AI is rapidly transforming computing, unlocking new use cases and turbocharging existing ones. Large language models (LLMs), such as OpenAI��s GPT...

Generative AI is rapidly transforming computing, unlocking new use cases and turbocharging existing ones. Large language models (LLMs), such as OpenAI��s GPT models and Meta��s Llama 2, skillfully perform a variety of tasks on text-based content. These tasks include summarization, translation, classification, and generation of new content such as computer code, marketing copy, poetry, and much more.

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Ashraf Eassa <![CDATA[Leading MLPerf Inference v3.1 Results with NVIDIA GH200 Grace Hopper Superchip Debut]]> http://www.open-lab.net/blog/?p=70450 2023-09-22T16:17:33Z 2023-09-09T16:00:00Z AI is transforming computing, and inference is how the capabilities of AI are deployed in the world��s applications. Intelligent chatbots, image and video...]]> AI is transforming computing, and inference is how the capabilities of AI are deployed in the world��s applications. Intelligent chatbots, image and video...NVIDIA Jetson Orin modules.

AI is transforming computing, and inference is how the capabilities of AI are deployed in the world��s applications. Intelligent chatbots, image and video synthesis from simple text prompts, personalized content recommendations, and medical imaging are just a few examples of AI-powered applications. Inference workloads are both computationally demanding and diverse, requiring that platforms be��

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Ashraf Eassa <![CDATA[New MLPerf Inference Network Division Showcases NVIDIA InfiniBand and GPUDirect RDMA Capabilities]]> http://www.open-lab.net/blog/?p=67021 2023-07-27T18:54:26Z 2023-07-06T16:00:00Z In MLPerf Inference v3.0, NVIDIA made its first submissions to the newly introduced Network division, which is now part of the MLPerf Inference Datacenter...]]> In MLPerf Inference v3.0, NVIDIA made its first submissions to the newly introduced Network division, which is now part of the MLPerf Inference Datacenter...Image of Infiniband with decorative images in front.

In MLPerf Inference v3.0, NVIDIA made its first submissions to the newly introduced Network division, which is now part of the MLPerf Inference Datacenter suite. The Network division is designed to simulate a real data center setup and strives to include the effect of networking��including both hardware and software��in end-to-end inference performance. In the Network division��

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Ashraf Eassa <![CDATA[Breaking MLPerf Training Records with NVIDIA H100 GPUs]]> http://www.open-lab.net/blog/?p=66919 2023-07-13T19:00:28Z 2023-06-27T16:00:00Z At the heart of the rapidly expanding set of AI-powered applications are powerful AI models. Before these models can be deployed, they must be trained through a...]]> At the heart of the rapidly expanding set of AI-powered applications are powerful AI models. Before these models can be deployed, they must be trained through a...Data center

At the heart of the rapidly expanding set of AI-powered applications are powerful AI models. Before these models can be deployed, they must be trained through a process that requires an immense amount of AI computing power. AI training is also an ongoing process, with models constantly retrained with new data to ensure high-quality results. Faster model training means that AI-powered applications��

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Ashraf Eassa <![CDATA[Setting New Records in MLPerf Inference v3.0 with Full-Stack Optimizations for AI]]> http://www.open-lab.net/blog/?p=62958 2023-07-05T19:23:50Z 2023-04-05T19:10:55Z The most exciting computing applications currently rely on training and running inference on complex AI models, often in demanding, real-time deployment...]]> The most exciting computing applications currently rely on training and running inference on complex AI models, often in demanding, real-time deployment...

The most exciting computing applications currently rely on training and running inference on complex AI models, often in demanding, real-time deployment scenarios. High-performance, accelerated AI platforms are needed to meet the demands of these applications and deliver the best user experiences. New AI models are constantly being invented to enable new capabilities��

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Sukru Burc Eryilmaz <![CDATA[Tuning AI Infrastructure Performance with MLPerf HPC v2.0 Benchmarks]]> http://www.open-lab.net/blog/?p=56880 2023-07-05T19:12:15Z 2022-11-09T18:00:00Z As the fusion of AI and simulation accelerates scientific discovery, the need has arisen for a means to measure and rank the speed and throughput for building...]]> As the fusion of AI and simulation accelerates scientific discovery, the need has arisen for a means to measure and rank the speed and throughput for building...

As the fusion of AI and simulation accelerates scientific discovery, the need has arisen for a means to measure and rank the speed and throughput for building AI models of the world��s supercomputers. MLPerf HPC, now in its third iteration, has emerged as an industry-standard measure of system performance using workloads traditionally performed on supercomputers.

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Sukru Burc Eryilmaz <![CDATA[Leading MLPerf Training 2.1 with Full Stack Optimizations for AI]]> http://www.open-lab.net/blog/?p=57148 2023-07-05T19:26:09Z 2022-11-09T18:00:00Z MLPerf benchmarks, developed by MLCommons, are critical evaluation tools for organizations to measure the performance of their machine learning models' training...]]> MLPerf benchmarks, developed by MLCommons, are critical evaluation tools for organizations to measure the performance of their machine learning models' training...

MLPerf benchmarks, developed by MLCommons, are critical evaluation tools for organizations to measure the performance of their machine learning models�� training across workloads. MLPerf Training v2.1��the seventh iteration of this AI training-focused benchmark suite��tested performance across a breadth of popular AI use cases, including the following: Many AI applications take advantage of��

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Ashraf Eassa <![CDATA[Full-Stack Innovation Fuels Highest MLPerf Inference 2.1 Results for NVIDIA]]> http://www.open-lab.net/blog/?p=54638 2023-07-05T19:26:31Z 2022-09-08T18:10:00Z Today��s AI-powered applications are enabling richer experiences, fueled by both larger and more complex AI models as well as the application of many models in...]]> Today��s AI-powered applications are enabling richer experiences, fueled by both larger and more complex AI models as well as the application of many models in...

Today��s AI-powered applications are enabling richer experiences, fueled by both larger and more complex AI models as well as the application of many models in a pipeline. To meet the increasing demands of AI-infused applications, an AI platform must not only deliver high performance but also be versatile enough to deliver that performance across a diverse range of AI models.

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Ashraf Eassa <![CDATA[The Full Stack Optimization Powering NVIDIA MLPerf Training v2.0 Performance]]> http://www.open-lab.net/blog/?p=49597 2023-07-05T19:27:00Z 2022-06-30T18:00:00Z MLPerf benchmarks are developed by a consortium of AI leaders across industry, academia, and research labs, with the aim of providing standardized, fair, and...]]> MLPerf benchmarks are developed by a consortium of AI leaders across industry, academia, and research labs, with the aim of providing standardized, fair, and...

MLPerf benchmarks are developed by a consortium of AI leaders across industry, academia, and research labs, with the aim of providing standardized, fair, and useful measures of deep learning performance. MLPerf training focuses on measuring time to train a range of commonly used neural networks for the following tasks: Lower training times are important to speed time to deployment��

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Maggie Zhang <![CDATA[Accelerating AI Inference Workloads with NVIDIA A30 GPU]]> http://www.open-lab.net/blog/?p=47944 2022-08-30T18:58:43Z 2022-05-11T22:43:14Z NVIDIA A30 GPU is built on the latest NVIDIA Ampere Architecture to accelerate diverse workloads like AI inference at scale, enterprise training, and HPC...]]> NVIDIA A30 GPU is built on the latest NVIDIA Ampere Architecture to accelerate diverse workloads like AI inference at scale, enterprise training, and HPC...

NVIDIA A30 GPU is built on the latest NVIDIA Ampere Architecture to accelerate diverse workloads like AI inference at scale, enterprise training, and HPC applications for mainstream servers in data centers. The A30 PCIe card combines the third-generation Tensor Cores with large HBM2 memory (24 GB) and fast GPU memory bandwidth (933 GB/s) in a low-power envelope (maximum 165 W).

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Dave Salvator <![CDATA[Getting the Best Performance on MLPerf Inference 2.0]]> http://www.open-lab.net/blog/?p=46305 2023-07-05T19:28:16Z 2022-04-07T00:26:22Z Models like Megatron 530B are expanding the range of problems AI can address. However, as models continue to grow complexity, they pose a twofold challenge for...]]> Models like Megatron 530B are expanding the range of problems AI can address. However, as models continue to grow complexity, they pose a twofold challenge for...

Models like Megatron 530B are expanding the range of problems AI can address. However, as models continue to grow complexity, they pose a twofold challenge for AI compute platforms: What��s needed is a versatile AI platform that can deliver the needed performance on a wide variety of models for both training and inference. To evaluate that performance, MLPerf is the only industry��

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Ashraf Eassa <![CDATA[Saving Time and Money in the Cloud with the Latest NVIDIA-Powered Instances]]> http://www.open-lab.net/blog/?p=44315 2023-07-05T19:28:41Z 2022-03-01T19:13:57Z AI is transforming every industry, enabling powerful new applications and use cases that simply weren��t possible with traditional software. As AI continues to...]]> AI is transforming every industry, enabling powerful new applications and use cases that simply weren��t possible with traditional software. As AI continues to...

AI is transforming every industry, enabling powerful new applications and use cases that simply weren��t possible with traditional software. As AI continues to proliferate, and with the size and complexity of AI models on the rise, significant advances in AI compute performance are required to keep up. That��s where the NVIDIA platform comes in. With a full-stack approach spanning chips��

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Vinh Nguyen <![CDATA[Boosting NVIDIA MLPerf Training v1.1 Performance with Full Stack Optimization]]> http://www.open-lab.net/blog/?p=41919 2023-07-05T19:29:06Z 2021-12-01T21:33:20Z Five months have passed since v1.0, so it is time for another round of the MLPerf training benchmark. In this v1.1 edition, optimization over the entire...]]> Five months have passed since v1.0, so it is time for another round of the MLPerf training benchmark. In this v1.1 edition, optimization over the entire...

Five months have passed since v1.0, so it is time for another round of the MLPerf training benchmark. In this v1.1 edition, optimization over the entire hardware and software stack sees continuing improvement across the benchmarking suite for the submissions based on NVIDIA platform. This improvement is observed consistently at all different scales, from single machines all the way to industrial��

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Sukru Burc Eryilmaz <![CDATA[MLPerf HPC v1.0: Deep Dive into Optimizations Leading to Record-Setting NVIDIA Performance]]> http://www.open-lab.net/blog/?p=41306 2023-07-05T19:29:32Z 2021-11-17T16:00:00Z In MLPerf HPC v1.0, NVIDIA-powered systems won four of five new industry metrics focused on AI performance in HPC. As an industry-wide AI...]]> In MLPerf HPC v1.0, NVIDIA-powered systems won four of five new industry metrics focused on AI performance in HPC. As an industry-wide AI...Data server room. Courtesy of Forschungszentrum J��lich/Sascha Kreklau.

In MLPerf HPC v1.0, NVIDIA-powered systems won four of five new industry metrics focused on AI performance in HPC. As an industry-wide AI consortium, MLPerf HPC evaluates a suite of performance benchmarks covering a range of widely used AI workloads. In this round, NVIDIA delivered 5x better results for CosmoFlow, and 7x more performance on DeepCAM, compared to strong scaling results from��

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Dave Salvator <![CDATA[Furthering NVIDIA Performance Leadership with MLPerf Inference 1.1 Results]]> http://www.open-lab.net/blog/?p=37689 2023-07-05T19:30:25Z 2021-09-22T17:00:00Z AI continues to drive breakthrough innovation across industries, including consumer Internet, healthcare and life sciences, financial services, retail,...]]> AI continues to drive breakthrough innovation across industries, including consumer Internet, healthcare and life sciences, financial services, retail,...

AI continues to drive breakthrough innovation across industries, including consumer Internet, healthcare and life sciences, financial services, retail, manufacturing, and supercomputing. Researchers continue to push the boundaries of what��s possible with rapidly evolving models that are growing in size, complexity, and diversity. In addition, many of these complex, large-scale models need to��

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Vinh Nguyen <![CDATA[MLPerf v1.0 Training Benchmarks: Insights into a Record-Setting NVIDIA Performance]]> http://www.open-lab.net/blog/?p=33929 2023-07-05T19:31:00Z 2021-06-30T17:00:00Z MLPerf is an industry-wide AI consortium tasked with developing a suite of performance benchmarks that cover a range of leading AI workloads widely in use. The...]]> MLPerf is an industry-wide AI consortium tasked with developing a suite of performance benchmarks that cover a range of leading AI workloads widely in use. The...

MLPerf is an industry-wide AI consortium tasked with developing a suite of performance benchmarks that cover a range of leading AI workloads widely in use. The latest MLPerf v1.0 training round includes vision, language and recommender systems, and reinforcement learning tasks. It is continually evolving to reflect the state-of-the-art AI applications. NVIDIA submitted MLPerf v1.0��

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Dave Salvator <![CDATA[Extending NVIDIA Performance Leadership with MLPerf Inference 1.0 Results]]> http://www.open-lab.net/blog/?p=30931 2023-09-19T16:28:44Z 2021-04-22T17:22:00Z Inference is where we interact with AI. Chat bots, digital assistants, recommendation engines, fraud protection services, and other applications that you use...]]> Inference is where we interact with AI. Chat bots, digital assistants, recommendation engines, fraud protection services, and other applications that you use...

Inference is where we interact with AI. Chat bots, digital assistants, recommendation engines, fraud protection services, and other applications that you use every day��all are powered by AI. Those deployed applications use inference to get you the information that you need. Given the wide array of usages for AI inference, evaluating performance poses numerous challenges for developers and��

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Shar Narasimhan <![CDATA[Updating AI Product Performance from Throughput to Time-To-Solution]]> http://www.open-lab.net/blog/?p=22364 2023-07-05T19:33:54Z 2020-11-23T17:00:06Z Data scientists and researchers work toward solving the grand challenges of humanity with AI projects such as developing autonomous cars or nuclear fusion...]]> Data scientists and researchers work toward solving the grand challenges of humanity with AI projects such as developing autonomous cars or nuclear fusion...

Data scientists and researchers work toward solving the grand challenges of humanity with AI projects such as developing autonomous cars or nuclear fusion energy research. They depend on powerful, high-performance AI platforms as essential tools to conduct their work. Even enterprise-grade AI implementation efforts��adding intelligent video analytics to existing video camera streams or image��

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Dave Salvator <![CDATA[Winning MLPerf Inference 0.7 with a Full-Stack Approach]]> http://www.open-lab.net/blog/?p=21799 2023-07-05T19:35:26Z 2020-10-21T17:09:36Z Three trends continue to drive the AI inference market for both training and inference: growing data sets, increasingly complex and diverse networks, and...]]> Three trends continue to drive the AI inference market for both training and inference: growing data sets, increasingly complex and diverse networks, and...

Three trends continue to drive the AI inference market for both training and inference: growing data sets, increasingly complex and diverse networks, and real-time AI services. MLPerf Inference 0.7, the most recent version of the industry-standard AI benchmark, addresses these three trends, giving developers and organizations useful data to inform platform choices, both in the datacenter and at��

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Nefi Alarcon <![CDATA[NVIDIA Breaks AI Performance Records in Latest MLPerf Benchmarks]]> https://news.www.open-lab.net/?p=17586 2023-07-05T19:37:26Z 2020-07-30T03:49:00Z NVIDIA today announced the fastest AI training performance among commercially available products, according to MLPerf benchmarks.  The A100 Tensor Core GPU...]]> NVIDIA today announced the fastest AI training performance among commercially available products, according to MLPerf benchmarks.  The A100 Tensor Core GPU...

NVIDIA today announced the fastest AI training performance among commercially available products, according to MLPerf benchmarks. The A100 Tensor Core GPU demonstrated the fastest performance per accelerator on all eight MLPerf benchmarks. The DGX SuperPOD system, a massive cluster of DGX A100 systems connected with HDR InfiniBand, also set eight new performance milestones.

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Akhil Docca <![CDATA[Accelerating AI Training with MLPerf Containers and Models from NVIDIA NGC]]> http://www.open-lab.net/blog/?p=19139 2023-07-05T19:37:55Z 2020-07-29T17:00:00Z The MLPerf consortium mission is to ��build fair and useful benchmarks�� to provide an unbiased training and inference performance reference for ML hardware,...]]> The MLPerf consortium mission is to ��build fair and useful benchmarks�� to provide an unbiased training and inference performance reference for ML hardware,...

The MLPerf consortium mission is to ��build fair and useful benchmarks�� to provide an unbiased training and inference performance reference for ML hardware, software, and services. MLPerf Training v0.7 is the third instantiation for training and continues to evolve to stay on the cutting edge. This round consists of eight different workloads that cover a broad diversity of use cases��

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Ivan Goldwasser <![CDATA[Optimizing NVIDIA AI Performance for MLPerf v0.7 Training]]> http://www.open-lab.net/blog/?p=19195 2023-07-05T19:38:22Z 2020-07-29T17:00:00Z MLPerf is an industry-wide AI consortium that has developed a suite of performance benchmarks covering a range of leading AI workloads that are widely in use...]]> MLPerf is an industry-wide AI consortium that has developed a suite of performance benchmarks covering a range of leading AI workloads that are widely in use...

MLPerf is an industry-wide AI consortium that has developed a suite of performance benchmarks covering a range of leading AI workloads that are widely in use today. The latest MLPerf v0.7 training submission includes vision, language, recommenders, and reinforcement learning. NVIDIA submitted MLPerf v0.7 training results for all eight tests and the NVIDIA platform set records in all��

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Dave Salvator <![CDATA[Int4 Precision for AI Inference]]> http://www.open-lab.net/blog/?p=15821 2023-02-13T17:33:48Z 2019-11-06T18:00:57Z INT4 Precision Can Bring an Additional 59% Speedup Compared to INT8 If there��s one constant in AI and deep learning, it��s never-ending optimization to wring...]]> INT4 Precision Can Bring an Additional 59% Speedup Compared to INT8 If there��s one constant in AI and deep learning, it��s never-ending optimization to wring...

If there��s one constant in AI and deep learning, it��s never-ending optimization to wring every possible bit of performance out of a given platform. Many inference applications benefit from reduced precision, whether it��s mixed precision for recurrent neural networks (RNNs) or INT8 for convolutional neural networks (CNNs), where applications can get 3x+ speedups. NVIDIA��s Turing architecture��

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Dave Salvator <![CDATA[MLPerf Inference: NVIDIA Innovations Bring Leading Performance]]> http://www.open-lab.net/blog/?p=15851 2023-07-05T19:38:49Z 2019-11-06T18:00:22Z New TensorRT 6 Features Combine with Open-Source Plugins to Further Accelerate Inference? Inference is where AI goes to work. Identifying diseases. Answering...]]> New TensorRT 6 Features Combine with Open-Source Plugins to Further Accelerate Inference? Inference is where AI goes to work. Identifying diseases. Answering...

Inference is where AI goes to work. Identifying diseases. Answering questions. Recommending products and services. The inference market is also diffuse, and will happen everywhere from the data center to edge to IoT devices across multiple use-cases including image, speech and recommender systems to name a few. As a result, creating a benchmark to measure the performance of these diverse platforms��

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Dave Salvator <![CDATA[NVIDIA Boosts AI Performance in MLPerf v0.6]]> http://www.open-lab.net/blog/?p=15214 2023-07-05T19:40:17Z 2019-07-10T17:00:26Z The relentless pace of innovation is most apparent in the AI domain. Researchers and developers discovering new network architectures, algorithms and...]]> The relentless pace of innovation is most apparent in the AI domain. Researchers and developers discovering new network architectures, algorithms and...

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Robert Sohigian <![CDATA[NVIDIA DGX SuperPOD Delivers World Record Supercomputing to Any Enterprise]]> http://www.open-lab.net/blog/?p=14829 2022-08-21T23:39:30Z 2019-06-17T07:01:20Z The NVIDIA DGX SuperPOD™?simplifies how the world approaches supercomputing, delivering world-record setting performance that can now be acquired by...]]> The NVIDIA DGX SuperPOD™?simplifies how the world approaches supercomputing, delivering world-record setting performance that can now be acquired by...

The NVIDIA DGX SuperPOD simplifies how the world approaches supercomputing, delivering world-record setting performance that can now be acquired by every enterprise in weeks instead of years. NVIDIA sets the bar once again in supercomputing, building a well-balanced system with 96 NVIDIA? DGX-2H servers containing 1,536 NVIDIA Tesla? V100 SXM3 GPUs. The DGX SuperPOD has earned the 22nd spot on the��

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Nefi Alarcon <![CDATA[NVIDIA Captures Top Spots on World��s First Industry-Wide AI Benchmark]]> https://news.www.open-lab.net/?p=12325 2023-07-05T19:42:16Z 2018-12-12T19:59:55Z Today, the MLPerf consortium published its first results for the seven tests that currently comprise this new industry-standard benchmark for machine learning....]]> Today, the MLPerf consortium published its first results for the seven tests that currently comprise this new industry-standard benchmark for machine learning....

Today, the MLPerf consortium published its first results for the seven tests that currently comprise this new industry-standard benchmark for machine learning. For the six test categories where NVIDIA submitted results, we��re excited to tell you that NVIDIA platforms have finished with leading single-node and at-scale results for all six, a testament to our total platform approach to accelerating��

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