mGPU Compute: Powering Machine-Learning & High-Performance Computing (HPC)
NVIDIA A100 Tensor Core GPUs offer exceptional throughput and low-latency networking for industry-leading performance.
Introduction
Unprecedented Acceleration
at Every Scale
Our mCloud platform utilises NVIDIA A100 Tensor Core GPUs to empower our users to be able to perform deep learning, HPC, and data analytics tasks with high-performance compute.
Representing the most powerful end-to-end AI and HPC platform for data centers, it allows technologists and researchers to deliver real-world results and deploy solutions into production at scale
With Multi-Instance GPU (MIG), the A100 can scale up efficiently or be divided into seven isolated GPU instances, offering a versatile platform that adapts dynamically to changing workload demands.
Features
Key Benefits of NVIDIA A100 GPUs
NVIDIA Ampere Architecture
Whether using MIG to partition an A100 GPU into smaller instances or NVLink to connect multiple GPUs to speed large-scale workloads, A100 can readily handle different-sized acceleration needs, from the smallest job to the biggest multi-node workload. A100’s versatility means IT managers can maximize the utility of every GPU in their data center, around the clock. .
Third-Generation Tensor Cores
NVIDIA A100 delivers 312 teraFLOPS (TFLOPS) of deep learning performance. That’s 20X the Tensor floating-point operations per second (FLOPS) for deep learning training and 20X the Tensor tera operations per second (TOPS) for deep learning inference compared to NVIDIA Volta GPUs
Next-Generation NVLink
NVIDIA NVLink in A100 delivers 2X higher throughput compared to the previous generation. When combined with NVIDIA NVSwitch™, up to 16 A100 GPUs can be interconnected at up to 600 gigabytes per second (GB/sec), unleashing the highest application performance possible on a single server. NVLink is available in A100 SXM GPUs via HGX A100 server boards and in PCIe GPUs via an NVLink Bridge for up to 2 GPUs.
Multi-Instance GPU (MIG)
An A100 GPU can be partitioned into as many as seven GPU instances, fully isolated at the hardware level with their own high-bandwidth memory, cache, and compute cores. MIG gives developers access to breakthrough acceleration for all their applications, and IT administrators can offer right-sized GPU acceleration for every job, optimizing utilization and expanding access to every user and application.
High-Bandwidth Memory (HBM2E)
With up to 80 gigabytes of HBM2e, A100 delivers the world’s fastest GPU memory bandwidth of over 2TB/s, as well as a dynamic randomaccess memory (DRAM) utilization efficiency of 95%. A100 delivers 1.7X higher memory bandwidth over the previous generation.
Structural Sparsity
AI networks have millions to billions of parameters. Not all of these parameters are needed for accurate predictions, and some can be converted to zeros, making the models “sparse” without compromising accuracy. Tensor Cores in A100 can provide up to 2X higher performance for sparse models. While the sparsity feature more readily benefits AI inference, it can also improve the performance of model training.
Artificial Intelligence
Deep Learning Training
AI models are exploding in complexity as they take on next-level challenges such as conversational AI. Training them requires massive compute power and scalability.
NVIDIA A100 Tensor Cores with Tensor Float (TF32) provide up to 20X higher performance over the NVIDIA Volta with zero code changes and an additional 2X boost with automatic mixed precision and FP16. When combined with NVIDIA® NVLink®, NVIDIA NVSwitch™, PCI Gen4, NVIDIA® InfiniBand®, and the NVIDIA Magnum IO™ SDK, it’s possible to scale to thousands of A100 GPUs.
For the largest models with massive data tables like deep learning recommendation models (DLRM), A100 80GB reaches up to 1.3 TB of unified memory per node and delivers up to a 3X throughput increase over A100 40GB.
Conclusion
Experience GPU-Accelerated
Cloud Computing
Our mCloud platform, built on robust OpenStack architecture, now offers the ability to integrate powerful NVIDIA GPUs directly into your virtual machines through GPU passthrough technology. This allows virtual machines to access the full capabilities of a physical GPU as if it were directly attached to the system, bypassing the hypervisor’s emulation layer and providing near-native performance.
Enhance your cloud capabilities with GPU acceleration by integrating dedicated GPUs into your mCloud virtual machines and take your computing to the next level.
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