10/16/2026
Stop wasting expensive GPU resources. If your inference models aren't using the full VRAM of your A100 or H100, you're leaving compute on the table.
NVIDIA's Multi-Instance GPU (MIG) lets you partition a single physical data-center GPU into up to seven fully isolated instances, each with its own dedicated compute, memory, and bandwidth.
We just published a complete, step-by-step tutorial on configuring MIG on a Linux dedicated server. In this guide, you'll learn how to:
⚡ Enable MIG mode and choose the exact memory/compute profiles for your workloads.
⚡ Isolate applications with strict hardware-level boundaries (a major upgrade over standard time-slicing).
⚡ Route specific MIG slices directly into Docker containers for clean multi-tenant setups.
⚡ Persist your partitions across reboots using mig-parted (a crucial, often-missed step for Hopper and Blackwell GPUs).
Whether you're a hosting provider, a research team, or just looking to right-size several AI models onto a single card, this guide covers everything you need to maximize your hardware utilization.
Read the full configuration tutorial here:
👉 https://www.colobird.com/tutorials/howto/split-gpu-nvidia-mig-dedicated-server/
