Audience

AI researchers, data scientists, and HPC developers needing a tool to eliminate I/O bottlenecks in multi-GPU, multi-node environments

About NVIDIA Magnum IO

NVIDIA Magnum IO is the architecture for parallel, intelligent data center I/O. It maximizes storage, network, and multi-node, multi-GPU communications for the world’s most important applications, using large language models, recommender systems, imaging, simulation, and scientific research. Magnum IO utilizes storage I/O, network I/O, in-network compute, and I/O management to simplify and speed up data movement, access, and management for multi-GPU, multi-node systems. It supports NVIDIA CUDA-X libraries and makes the best use of a range of NVIDIA GPU and networking hardware topologies to achieve optimal throughput and low latency. In multi-GPU, multi-node systems, slow CPU, single-thread performance is in the critical path of data access from local or remote storage devices. With storage I/O acceleration, the GPU bypasses the CPU and system memory, and accesses remote storage via 8x 200 Gb/s NICs, achieving up to 1.6 TB/s of raw storage bandwidth.

Integrations

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Company Information

NVIDIA
Founded: 1993
United States
www.nvidia.com/en-us/data-center/magnum-io/

Videos and Screen Captures

NVIDIA Magnum IO Screenshot 1
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Product Details

Platforms Supported
Cloud
Training
Documentation
Live Online
Webinars
In Person
Videos
Support
Phone Support
Online

NVIDIA Magnum IO Frequently Asked Questions

Q: What kinds of users and organization types does NVIDIA Magnum IO work with?
Q: What languages does NVIDIA Magnum IO support in their product?
Q: What kind of support options does NVIDIA Magnum IO offer?
Q: What other applications or services does NVIDIA Magnum IO integrate with?
Q: What type of training does NVIDIA Magnum IO provide?

NVIDIA Magnum IO Product Features

Data Center Management

Power Management
Audit Trail
Diagnostic Testing
Import / Export Data
Device Auto Discovery
Sarbanes-Oxley Compliance
Multi-User
JCL Management
Behavior-Based Acceleration
Cross Reference System
Multi-Platform