ssh {SSH_TARGET} "nvidia-smi --query-gpu=index,name,memory.total,memory.used,memory.free,utilization.gpu --format=csv,noheader,nounits"

ssh {SSH_TARGET} "nvidia-smi --query-compute-apps=pid,gpu_uuid,used_memory,name --format=csv,noheader,nounits"

ssh {SSH_TARGET} "nvidia-smi --query-gpu=index,gpu_uuid --format=csv,noheader"

ssh {SSH_TARGET} "docker ps --format '{{.ID}} {{.Names}}' 2>/dev/null"

ssh {SSH_TARGET} "for cid in \$(docker ps -q); do name=\$(docker inspect --format '{{.Name}}' \$cid | sed 's/^\///'); pids=\$(docker top \$cid -o pid 2>/dev/null | tail -n +2); for p in \$pids; do echo \"\$p \$name\"; done; done 2>/dev/null"

ssh {SSH_TARGET} "for cid in \$(docker ps -q); do name=\$(docker inspect --format '{{.Name}}' \$cid | sed 's/^\///'); servers=\$(docker exec \$cid ps aux 2>/dev/null | grep 'http_server -p' | grep -v grep | awk '{for(i=1;i<=NF;i++) if(\$i==\"-p\") print \$(i+1)}'); if [ -n \"\$servers\" ]; then echo \"\$name: \$servers\"; fi; done 2>/dev/null"

## GPU Usage Overview

| GPU | Model | Video Memory Usage | Free | GPU Utilization | Status |
|-----|------|----------|------|------------|------|
| 0 | H200 | 107 / 141 GB | 34 GB | 85% | 🔴 Busy |
| 1 | H200 | 12 / 141 GB | 129 GB | 10% | 🟢 Idle |
| 2 | H200 | 0 / 141 GB | 141 GB | 0% | ⚪ No Task |

## Process Details

| GPU | Video Memory Usage | Container | Process |
|-----|----------|------|------|
| 0 | 107 GB | vllm_qwen35 | VLLM::EngineCore |
| 0 | 2 GB | truetranslate-api-bin | truetranslate_api.bin |
| 1 | 12 GB | atlas_video | python |

## Multi-instance Services (Single-container Multi-terminal Deployment)

If multiple `http_server` instances are detected running in a container, list them separately:

| Container | Port | GPU | Status |
|------|------|-----|------|
| atlas_video | :5001 | GPU 2 | Running |
| atlas_video | :5002 | GPU 3 | Running |

## Idle Resources

GPUs available for new service deployment:
- GPU 4: 141 GB fully idle
- GPU 5: 141 GB fully idle