SEVEN INDEPENDENT INSTANCES ON A SINGLE GPU

Multi-Instance GPU(MIG) extends the performance and value of every NVIDIA H100, A100 and A30 Graphics processor. MIG can partition the A100 GPU into up to seven instances, each fully isolated and with its own high-bandwidth memory, cache, and compute cores. Now administrators can support any workload, from the smallest to the largest, and right-size a GPU for each job with guaranteed quality of service (QoS). It can also optimize workloads and extend the reach of accelerated compute resources to every user.

PERFORMANCE OVERVIEW

EXTENSION OF ACCESS TO GPUS
for MORE USERS

With MIG, you can achieve up to 7x more GPU resources on a single A100 GPU. MIG gives researchers and developers more resources and flexibility than ever before.

OPTIMIZE GPU UTILIZATION

MIG provides the flexibility to select many different instance sizes, enabling the deployment of right-sized GPU instances for each workload, ultimately resulting in optimal utilization and maximization of data center investments.

enable EXecution oF
mIXed workloads

MIG enables simultaneous execution of inference, training, and high-performance computing (HPC) workloads on a single GPU with deterministic latency and throughput.

HOW THE TECHNOLOGY WORKS

Without MIG, different tasks like AI inference queries running on the same GPU compete for the same resources (e.g. memory bandwidth). A task that consumes more memory bandwidth starves others, causing multiple tasks to miss their latency targets. With MIG, tasks run concurrently on different instances, each with dedicated compute, memory, and memory bandwidth resources, resulting in predictable performance with high quality of service and maximized GPU utilization.
Nvidia Mig HPC demo

ACHIEVING ULTIMATE FLEXIBILITY
IN THE DATA CENTER

An NVIDIA A100 GPU can be partitioned into MIG instances of different sizes. For example, an administrator could create two instances with 20 gigabytes (GB) of memory each, or three instances with 10 GB, or seven instances with 5 GB, or a mixture of both. This allows the system administrator to provide GPUs of the right size to users for different types of workloads.

MIG instances can also be dynamically reconfigured, allowing administrators to shift GPU resources base on changing user and business needs. For example, seven MIG instances can be used for low-throughput conferencing during the day and reconfigured into one large MIG instance for deep learning training at night.

EXCEPTIONAL QUALITY OF SERVICE

Each MIG instance has a dedicated set of hardware resources for data processing, memory and cache that provide guaranteed quality of service (QoS) and fault isolation for the workload. This means that a fault in an application running on one instance will not affect applications running on other instances. In addition, different types of workloads can run on different instances: interactive model development, deep learning training, AI inference, or HPC applications. Since the instances run in parallel, the workloads also run in parallel, but separately and isolated on the same physical A100 GPU.

MIG is ideal for workloads such as low-latency AI model development and inferencing. These workloads can take full advantage of the A100's features and fit into the memory allocated to each instance.
Multi instance GPU workflow diagram

MIG in NVIDIA H100

Based on NVIDIA Hopper™ architecture, the H100 extends MIG by supporting multi-tenant, multi-user configurations in virtualized environments for up to seven GPU instances, with each instance securely isolated at the hardware and hypervisor level through Confidential Computing. Dedicated video decoders for each MIG instance enable high-throughput intelligent video analytics (IVA) on shared infrastructure. Hopper's concurrent MIG profiling allows administrators to monitor correctly sized GPU acceleration and allocate resources for multiple users.

Researchers with smaller workloads can use MIG instead of a full cloud instance to securely isolate a portion of a GPU, confident that their data is protected during storage, transfer and use. This increases the flexibility for cloud service providers to serve smaller potential customers and be more cost effective.

SEE MIG IN ACTION

BUILT FOR IT AND DEVOPS

MIG enables fine-grained GPU provisioning by IT and DevOps teams. Each MIG instance behaves like a standalone GPU for applications, eliminating the need to make changes to the CUDA® platform. MIG can be deployed in all major enterprise computing environments.

MIG specifications

H100
A100
Confidential computing
Yes
-
Instance types
7 x 10 GB
4 x 20 GB
2 x 40 GB (more computing capacity)
1 x 80 GB
7 x 10 GB
3 x 20 GB
2 x 40 GB
1 x 80 GB
GPU profiling and monitoring
Simultaneously on all instances
Only one instance at a time
Secure clients
7x
1x
Media decoder
Dedicated NVJPEG and NVDEC per instance
Limited options
Preliminary specifications, subject to change

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