09/25/2024 | News release | Distributed by Public on 09/25/2024 09:19
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.
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:
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 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: