NVIDIA MIG: A Practical Guide to GPU Partitioning for Efficient AI Infrastructure

As AI models grow rapidly in size, securing GPU resources has become a top priority for organizations.

However, high-performance GPUs come with significant upfront costs (CAPEX), and global supply constraints make timely acquisition difficult.
As a result, the key question is no longer just how many GPUs you have, but how efficiently you use them.

The Reality of GPU Resource Management in AI Environments

Even after securing expensive GPU resources, organizations often face operational challenges.

This becomes more evident in multi-tenant environments, where multiple teams share the same infrastructure.

Resource Lock-In and Bottlenecks

In traditional setups, a single workload is assigned to a full physical GPU.

For example, if a team reserves 5 GPUs but only uses around 3.1 GPUs in practice, the remaining capacity cannot be used by others.
This creates a structural inefficiency.

Over time, this leads to:

• lower overall utilization

• delayed development for other teams

• system-wide bottlenecks

Operational Cost and Infrastructure Burden

Excessive GPU allocation does not only waste compute resources.
It also increases:

• power consumption

• cooling costs

• overall operational expenses (OPEX)

From an infrastructure perspective, the challenge is clear:
maximize output while minimizing cost.

This is where hardware-level partitioning technology like NVIDIA MIG becomes essential.

Understanding NVIDIA MIG (Multi-Instance GPU)

MIG is a GPU partitioning technology introduced with NVIDIA’s Ampere (A100) and Hopper (H100) architectures.

Its core idea is simple: divide a single physical GPU into multiple independent instances.

Hardware-Level Resource Partitioning

With MIG, a single A100 GPU can be split into up to 7 independent instances.

Unlike software-based sharing, MIG allocates hardware resources directly to each instance:

• SM (Streaming Multiprocessor): The core 'brain' of the GPU responsible for all heavy-duty computation.

• L2 Cache & Memory Bandwidth: The high-speed temporary storage and the data highway through which information flows.

Each instance receives its own dedicated resources.

This means workloads do not interfere with each other, and each instance can maintain consistent performance — similar to using a dedicated GPU.

Fault Isolation for Stability

Another key advantage of MIG is fault isolation.

If one instance fails or a process crashes, the other instances remain unaffected.

This is similar to how one apartment losing power does not affect the entire building.

For enterprise environments, this level of isolation is critical for maintaining service quality.

Flexible Resource Reconfiguration

MIG also allows flexible resource allocation based on workload demand.

For example:

• during peak hours → split GPUs into smaller instances for inference

• during off-hours → combine instances for large-scale training

This flexibility allows infrastructure to adapt to changing workloads.

Beyond 7 Partitions: AI Pub and Coaster

While MIG provides strong hardware-level isolation, its limit of 7 partitions may not fully meet the needs of large organizations.

TEN addresses this limitation with a software-defined approach.

Fine-Grained GPU Allocation up to 100 Partitions

AIPub, powered by TEN’s Kubernetes-based engine Coaster, extends GPU resource allocation beyond hardware limits.

It supports up to 100 logical partitions through a block-based resource model.

This approach:

• builds on MIG’s physical isolation

• enables much finer resource allocation

• significantly improves utilization across users

Resource Group Management and Real-Time Monitoring

AI Pub also provides a structured way to manage resources.

Using resource groups, administrators can:

• define quotas based on organizational structure

• control resource allocation by team or project

Users can then operate within assigned limits.

All usage is visible in real time through a web-based UI, making it easy to track where and how resources are being used.

Conclusion: Efficient Resource Utilization Defines AI Competitiveness

In the era of LLMs, infrastructure competitiveness is no longer defined by the number of GPUs.

It is defined by how intelligently those resources are used.

The combination of NVIDIA MIG (hardware-level partitioning) & AI Pub (software-level orchestration and optimization) provides a practical path toward efficient AI infrastructure.

Balancing cost optimization and development productivity will continue to be a key challenge — and a key opportunity — in the AI market.

If you are looking to improve infrastructure efficiency, now is the time to explore a more optimized approach.

👉 Explore AIPub’s unified infrastructure strategy
📩 Talk to an expert