Maximizing GPU ROI: How to Partition NVIDIA A100 & H100 with MIG

 



Most AI teams provision GPUs the way they provision servers: one workload, one full device. But running a 7B-parameter inference endpoint or a batch embedding job doesn't require a massive 80GB of HBM3 memory.

When you run lightweight workloads on a full A100 or H100, most of the silicon sits idle—while your budget burns away.

NVIDIA’s Multi-Instance GPU (MIG) technology solves this by physically dividing a single GPU into up to 7 independent, hardware-isolated instances. Here is a quick breakdown of how it works and how you can implement it to maximize your hardware ROI.

Why Choose MIG Over Time-Slicing?

Unlike software-based sharing methods (like CUDA MPS or time-slicing) where processes cooperatively share resources, MIG offers true hardware-level isolation.

  • Dedicated Resources: Each MIG instance gets its own assigned Streaming Multiprocessors (SMs), memory, and L2 cache.

  • Zero Resource Contention: A massive, memory-heavy request on one instance cannot starve, slow down, or crash a lightweight model running on the neighbor instance.

  • Predictable Latency: Enterprise AI hosting requires strict latency guarantees. MIG ensures your throughput remains consistent regardless of tenant load.

A100 vs. H100 MIG Profiles at a Glance

NVIDIA allows you to partition your card using pre-defined configurations called "profiles." Depending on your specific workload, you can create a uniform geometry (identical slices) or a mixed geometry (different sized slices on one card).

  • NVIDIA A100 (40GB/80GB): Supports up to 7 concurrent instances (ranging from 1g.5gb configurations up to a full 7g.40gb slice).

  • NVIDIA H100 (80GB): Supports up to 7 concurrent instances (ranging from 1g.10gb up to a full 7g.80gb slice).

Practical Example: On a single H100 80GB, you can carve out one 3g.40gb instance to serve a mid-sized 13B model, and use the remaining capacity to spin up two independent 2g.20gb instances for embedding or reranking models. That's three separate production services running securely on one physical card!

Step-by-Step Implementation Outline

Configuring MIG is entirely CLI-driven using standard nvidia-smi commands. The basic workflow involves:

  1. Checking Current Status: Checking if MIG mode is active on your target GPU index.

  2. Enabling MIG Mode: Activating the hardware switch using sudo nvidia-smi -i 0 -mig 1.

  3. Creating Instances: Defining your GPU and Compute instances based on your desired profile IDs.

  4. Targeting Workloads: Using CUDA_VISIBLE_DEVICES or Docker container flags to pin specific models to specific hardware UUIDs.

🚀 Read the Full Step-by-Step Guide with Exact CLI Commands

Ready to set up your mixed geometry profiles, automate persistence across server reboots using mig-parted, and see the exact production Docker scripts?

We have published the complete technical walkthrough with copy-pasteable configuration scripts on our main platform.

👉 Click Here to Read the Full MIG Partitioning Tutorial on GPUYard

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