Nutanix Unified Storage wins the MLPerf storage v1.0 benchmark

Nutanix Unified Storage™ is now a leader in AI storage performance using the latest MLPerf™ Storage v1.0 benchmark, with a 2x performance improvement over last year's result, establishing NUS as a gold standard for AI and machine learning applications.

High-Performance Storage - A Key Component in Enterprise AI Infrastructure

As enterprises adopt AI (including generative AI or genAI), having a fast and efficient data storage system becomes critical. AI workloads are evolving, and many enterprises still focus on training AI models, inference (interacting and using a model) and tuning (updating an existing model and augmenting it with new data without re-training) are also key considerations when implementing enterprise AI. Regardless of your AI strategy, consider the following if training a model is part of your plan:

• Infrastructure requirements and cost
• Time-to-business value for a trained AI model
• A unified platform to not only train a model but also deploy and use it for enterprise AI applications

If training an AI model is essential to your business, choosing the right environment for the process is key. The public cloud offers a cost-effective option by allowing you to 'rent' AI accelerators (GPUs) without a large upfront investment. However, after training, you'll need to reevaluate if the public cloud is still the best option for inference or tuning.

Imagine having a solution that supports both hybrid AI needs - whether on-premises or in the cloud. The Nutanix Unified Storage (NUS) platform is the answer, delivering high-performance storage and consistent experience to run your AI apps across diverse environments, with a single license.

The Results Are In

The table below shows the storage performance of Nutanix Unified Storage (NUS) on-premises and in public cloud (AWS) with an image classification workload (resnet50). We tested two separate NUS cluster configurations: a 32-node cluster on AWS and a 7-node cluster on-premises, both serving files data to simulated NVIDIA H100 accelerators.

The results demonstrate the following:

• A single NUS cluster can serve 1056 accelerators, the highest of all vendors listed in the benchmark
• Performance scales linearly with the 32-node cluster supporting 4X accelerators as the 8-node cluster
• Similar performance per node is observed irrespective of the location, on-premises or in the cloud