dgx-spark-playbooks/nvidia/station-ai-skills/assets/skills/mig-configure/SKILL.md

name	description	metadata
mig-configure	Configure NVIDIA MIG (Multi-Instance GPU) partitions on the DGX Station GB300, including enabling MIG mode, choosing a profile layout, creating instances, and retrieving MIG UUIDs. Use when the user asks to partition the GB300, set up MIG, run multiple models in isolation on one GPU, or reconfigure existing MIG instances.	publisher hardware nvidia DGX Station GB300

MIG Configuration on DGX Station

Configure MIG (Multi-Instance GPU) partitions on the DGX Station GB300.

Steps

1. Find the GB300 GPU index. Run:

   nvidia-smi --query-gpu=index,name --format=csv,noheader
   

2. Check current MIG state:

   nvidia-smi -i <GB300_INDEX> -q | grep -i "MIG Mode"
   

3. If MIG is already enabled, show current instances:

   nvidia-smi mig -lgi -i <GB300_INDEX>
   nvidia-smi mig -lci -i <GB300_INDEX>
   

   If the user wants to reconfigure, destroy existing instances first (step 6).

4. If MIG is not enabled, enable it. All GPU processes must be stopped first:

   # Check for running GPU processes
   sudo fuser -v /dev/nvidia*
   
   # Enable MIG
   sudo nvidia-smi -i <GB300_INDEX> -mig 1
   
   # Verify
   nvidia-smi -i <GB300_INDEX> -q | grep -i "MIG Mode"
   

5. Show available profiles and help the user choose a layout:

   nvidia-smi mig -lgip -i <GB300_INDEX>
   

   Common GB300 MIG profiles:

   Profile	ID	Memory	Use case
   1g.35gb	19	~35 GB	Small models (7-8B), dev/test
   1g.35gb+me	20	~35 GB	Same + media extensions
   1g.70gb	15	~70 GB	Slightly larger inference
   2g.70gb	14	~70 GB	Medium models (14-30B)
   3g.139gb	9	~139 GB	Large models (70B quantized)
   4g.139gb	5	~139 GB	Large models, more compute
   7g.278gb	0	~278 GB	Full GPU as single instance

   Suggest layouts based on the user's workload. Examples:

   • Two models (70B + 8B): 3g.139gb + 2g.70gb + 2g.70gb → IDs 9,14,14
   • Many small models: 7 × 1g.35gb → IDs 19,19,19,19,19,19,19
   • One large model with isolation: 7g.278gb → ID 0

   Ask the user what models they want to run before suggesting a layout.

6. Create (or recreate) instances:

   If reconfiguring, destroy existing instances first:

   sudo nvidia-smi mig -dci -i <GB300_INDEX>
   sudo nvidia-smi mig -dgi -i <GB300_INDEX>
   

   Then create the new layout:

   sudo nvidia-smi mig -cgi <PROFILE_IDS> -C -i <GB300_INDEX>
   

7. Get the MIG device UUIDs:

   nvidia-smi -L
   

   Note the MIG-<uuid> entries — these are used to target specific MIG instances.

8. Show the user how to use MIG devices:

   # Bare metal
   export CUDA_VISIBLE_DEVICES=MIG-<uuid>
   
   # Docker
   docker run --gpus '"device=MIG-<uuid>"' ...
   

9. Report the final layout to the user with UUIDs and suggested docker commands for each instance.

Disabling MIG

If the user wants to return to full-GPU mode:

# Stop all workloads using MIG instances first
sudo nvidia-smi mig -dci -i <GB300_INDEX>
sudo nvidia-smi mig -dgi -i <GB300_INDEX>
sudo nvidia-smi -i <GB300_INDEX> -mig 0

# Ensure Fabric Manager is running for NVLink re-initialization
sudo systemctl start nvidia-fabricmanager