NVIDIA MULTI-INSTANCE GPU

Seven independent instances in a single GPU.

Multi-Instance GPU (MIG) expands the performance and value of NVIDIA Blackwell and Hopper™ GPUs. MIG can partition the GPU into as many as seven instances, each fully isolated with its own high-bandwidth memory, cache, and compute cores. This gives administrators the ability to support every workload, from the smallest to the largest, with guaranteed quality of service (QoS) and extending the reach of accelerated computing resources to every user.

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Extend the value of your GPU by enabling up to seven concurrent and fully isolated GPU instances

With multi-instance GPU technology, expand your team's workload and accomplish tasks more efficiently and in a timely manner. MIG allows for the partitioning of a single GPU into up to 7 isolated instances, each with its own fully independent memory, cache, and compute cores. This expands professionals' access to GPU resources, allowing for accelerated computing support and resources on every workload.

How the Technology Works

Without MIG, different jobs running on the same GPU, such as different AI inference requests, compete for the same resources. A job consuming larger memory bandwidth starves others, resulting in several jobs missing their latency targets. With MIG, jobs run simultaneously on different instances, each with dedicated resources for compute, memory, and memory bandwidth, resulting in predictable performance with QoS and maximum GPU utilization.

Provision and Configure Instances as Needed

A GPU can be partitioned into different-sized MIG instances. For example, on an NVIDIA RTX PRO™ 6000 Blackwell Workstation Edition, an administrator could create two instances with 48 GB of memory each, and four instances with 24 GB each.

MIG instances can also be dynamically reconfigured, enabling administrators to shift GPU resources in response to changing user and business demands. For example, seven MIG instances can be used during the day for low-throughput inference and reconfigured to one large MIG instance at night for deep learning training.

Run Workloads in Parallel, Securely

With a dedicated set of hardware resources for compute, memory, and cache, each MIG instance delivers guaranteed QoS and fault isolation. That means that a failure in an application running on one instance doesn’t impact applications running on other instances.

It also means that different instances can run different types of workloads—interactive model development, deep learning training, AI inference, or HPC applications. Since the instances run in parallel, the workloads also run in parallel—but separate and isolated—on the same physical GPU.

Expand GPU Access

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

Optimize GPU Utilization

MIG provides the flexibility to choose many different instance sizes, which allows provisioning of the right-sized GPU instance for each workload, ultimately optimizing utilization and maximizing data center investment.

Run Simultaneous Workloads

MIG enables inference, training, and high-performance computing (HPC) workloads to run at the same time on a single GPU with deterministic latency and throughput. Unlike time slicing, each workload runs in parallel, delivering higher performance.

Seven independent instances in a single GPU

NVIDIA Multi-Instance GPU

Multi-Instance GPU (MIG) expands the performance and value of NVIDIA Blackwell and Hopper™ generation GPUs. MIG can partition the GPU into as many as seven instances, each fully isolated with its own high-bandwidth memory, cache, and compute cores. This gives administrators the ability to support every workload, from the smallest to the largest, with guaranteed quality of service (QoS) and extending the reach of accelerated computing resources to every user.

Built for IT and DevOps

MIG enables fine-grained GPU provisioning by IT and DevOps teams. Each MIG instance behaves like a standalone GPU to applications, so there’s no change to the CUDA® platform. MIG can be used in all major enterprise computing environments.

Deploy from Data Center to Edge

Use MIG on premises, in the cloud, and at the edge.

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Leverage Containers

Run containerized applications on MIG instances.

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Support Kubernetes

Schedule Kubernetes pods on MIG instances.

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Virtualize Applications

Run applications on MIG instances inside a virtual machine.

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Product	Architecture	Compute Capability	Memory Size	Max Number of Instances
NVIDIA H200 NVL	Hopper	9	141 GB	7
NVIDIA RTX PRO 6000 Blackwell Server Edition	Blackwell	12	96 GB	4
NVIDIA RTX PRO 6000 Blackwell Workstation Edition	Blackwell	12	96 GB	4
NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition	Blackwell	12	96 GB	4
NVIDIA RTX PRO 5000 72GB Blackwell	Blackwell	12	72 GB	2
NVIDIA RTX PRO 5000 Blackwell	Blackwell	12	48 GB	2

*Sizes shown for NVIDIA RTX PRO™ Blackwell GPUs, for more information, please refer to technical documentation.

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