dgx-diagnose

name	dgx-diagnose
description	Diagnose common DGX Station GB300 issues — CUDA crashes, wrong-GPU targeting, vLLM/SGLang container bugs, MIG state problems, NVLink/Fabric Manager errors, X/Vulkan failures, HuggingFace auth, and port conflicts. Use when the user reports a GPU error, inference server crash, MIG problem, or any unexplained DGX Station failure.
metadata	{"publisher":"nvidia","hardware":"DGX Station GB300"}

DGX Station Diagnostics

Diagnose common DGX Station issues. Run through the checks below to identify the problem.

Step 1. Gather system state

Run these commands and analyze the output:

# GPU status
nvidia-smi

# GPU device list with indices
nvidia-smi --query-gpu=index,name,memory.used,memory.total --format=csv,noheader

# Driver version
nvidia-smi --query-gpu=driver_version --format=csv,noheader | head -1

# MIG state
nvidia-smi -i 1 -q 2>/dev/null | grep -i "MIG Mode" || echo "Could not query MIG on device 1"

# Fabric Manager
systemctl is-active nvidia-fabricmanager

# GPU processes
sudo fuser -v /dev/nvidia* 2>/dev/null || echo "No GPU processes found"

# Docker containers using GPUs
docker ps --format "table {{.Names}}\t{{.Image}}\t{{.Status}}" 2>/dev/null

Step 2. Match symptoms to known issues

Based on the gathered state and the user's reported problem, check for these known issues:

CUDA crashes with --gpus all

Cause: Mixed coherency — GB300 (ATS) and RTX PRO (non-ATS) cannot share a CUDA context. Fix: Use --gpus '"device=N"' targeting only the GB300.

Model running on wrong GPU (RTX PRO instead of GB300)

Check: The device index in the docker command vs actual GPU indices. Fix: Verify with nvidia-smi --query-gpu=index,name --format=csv,noheader and correct the --gpus flag.

vLLM crash / FlashInfer buffer overflow

Check: Container version — docker inspect vllm-server | grep Image Fix: Use nvcr.io/nvidia/vllm:26.01-py3. Version 25.10 has a known FlashInfer bug on DGX Station.

SGLang CUDA errors

Check: Container tag — must be cu130 for Blackwell SM103. Fix: Use lmsysorg/sglang:latest-cu130.

CUDA OOM despite 279 GB HBM

Check: --max-model-len / --context-length and memory utilization settings. Fix: Reduce context length or lower --gpu-memory-utilization / --mem-fraction-static.

nvidia-smi -mig 1 returns "In use by another client"

Check: sudo fuser -v /dev/nvidia* — GPU processes must be stopped first. Fix: Stop all GPU workloads, then retry.

NVLink errors after disabling MIG

Check: systemctl is-active nvidia-fabricmanager Fix: sudo systemctl start nvidia-fabricmanager

X server crash after nvidia-xconfig -a

Fix: sudo cp /etc/X11/xorg.conf.nvidia-xconfig-original /etc/X11/xorg.conf

Vulkan VK_ERROR_INITIALIZATION_FAILED

Cause: CUDA initialized before Vulkan, binding to GB300. Fix: Run CUDA and Vulkan workloads in separate processes. For Vulkan apps: __GL_DeviceModalityPreference=2 ./your_app

HuggingFace 401 / token errors

Fix: Pass token inline: -e HF_TOKEN="hf_...". Don't rely on shell export for background Docker tasks.

Port already in use

Check: lsof -i :<PORT> Fix: Stop the conflicting process or use a different host port: -p 8001:8000.

Step 3. Report findings

Tell the user:

1. What the issue is
2. Why it happens (root cause)
3. The specific command to fix it
4. How to verify the fix worked