homelab-cluster（家庭实验室集群）

Homelab Cluster Management

Manage a compound AI compute cluster spanning multiple tiers of GPU and CPU inference nodes. Built and battle-tested by Lumina Homelab.

When to Use

Use this skill when your agent needs to:

• Monitor health of distributed model endpoints
• Route inference requests to the best available model
• Recover downed nodes automatically
• Plan GPU memory allocation across models
• Deploy models across heterogeneous hardware

Architecture Pattern

A homelab cluster typically spans 2-3 tiers:

A LiteLLM proxy sits in front, providing a unified OpenAI-compatible API across all tiers.

Health Monitoring

Check all endpoints with configurable per-endpoint timeouts:

# Define endpoints with tier labels
ENDPOINTS = {
    "local/ollama": {"url": "http://localhost:11434/api/tags", "tier": "LOCAL"},
    "remote/mark-i": {"url": "http://REMOTE_IP:3009/v1/models", "tier": "REMOTE", "timeout": 8},
    "gateway/litellm": {"url": "http://localhost:8080/health/liveliness", "tier": "GATEWAY"},
}

# For each endpoint: GET with timeout, check HTTP 200
# Classify: HEALTHY / DEGRADED / DOWN per tier
# Overall prognosis based on tier health

Key lesson: Use /health/liveliness for LiteLLM, not /health — the latter probes all model routes and hangs if any are unreachable.

Expert MoE Routing

Route requests to the optimal model based on task classification:

Task Categories:
  code     → Coder model (Qwen2.5-Coder-7B or similar)
  reason   → Reasoning model (DeepSeek-R1-Distill or similar)
  chat     → General model (Qwen2.5-14B or similar)
  vision   → Vision model (Qwen2.5-VL or similar)
  fast     → Smallest available model for quick responses
  embed    → Embedding model (nomic-embed-text or similar)

Router logic:
  1. Classify task from prompt
  2. Check health of preferred model
  3. Fallback to next-best if unavailable
  4. Return model endpoint + metadata

Docker Deployment (llama.cpp on Remote Nodes)

Critical: Use Docker Volumes, Not Bind Mounts

For models larger than ~1.5GB on Windows Docker hosts:

# Create a Docker volume for model storage
docker volume create models-vol

# Copy models INTO the volume
docker run --rm -v models-vol:/models -v /host/path:/src alpine cp /src/model.gguf /models/

# Run container FROM volume (not bind mount)
docker run -d --gpus all -v models-vol:/models -p 3009:8000 \
  -e MODEL_PATH=/models/model.gguf your-llamacpp-image

Why: Windows bind mounts use gRPC-FUSE/9P bridge which hangs during GPU tensor loading for large files. Docker volumes use native Linux ext4 and bypass this entirely.

Sequential Container Startup

Never start multiple GPU containers simultaneously:

# WRONG — causes CUDA initialization deadlock
docker start mark-i mark-iii mark-iv mark-vi &

# RIGHT — sequential with health check between each
for container in mark-v mark-iii mark-iv mark-vi mark-i; do
  docker restart $container
  sleep 5
  # Verify health before starting next
  curl -s http://localhost:PORT/v1/models || echo "Warning: $container slow to start"
done

GPU Memory Planning

Plan your model lineup to fit within VRAM:

Example for 24GB GPU:
  14B model (Q4_K_M)  →  9.0 GB, 28 GPU layers
  7B coder            →  4.4 GB, full GPU
  8B reasoning        →  4.6 GB, full GPU
  1.5B fast coder     →  1.1 GB, full GPU
  1.7B fast chat      →  1.0 GB, full GPU
  ─────────────────────────────
  Total:               20.1 GB (~84% utilized)

  Remaining: CPU-only containers for 32B+ models

Automatic Node Recovery

When a remote node goes down (Docker Desktop crash, reboot, etc.):

Recovery sequence:
  1. Health check fails for remote tier
  2. Check if SSH is responsive (node is up but Docker is down)
  3. If SSH works: restart Docker Desktop via SSH
  4. If SSH fails: create RDP session to wake the machine
  5. Wait for Docker + sequential container restart
  6. Re-check health

Important: Never store recovery credentials in plaintext. Use a vault (Azure Key Vault, HashiCorp Vault, etc.) and pipe secrets through stdin, never as CLI arguments.

LiteLLM Gateway Configuration

Unified API across all tiers:

model_list:
  # Local Ollama models
  - model_name: local/chat
    litellm_params:
      model: ollama/qwen2.5:32b
      api_base: http://localhost:11434

  # Remote llama.cpp models (need openai/ prefix)
  - model_name: remote/mark-i
    litellm_params:
      model: openai/qwen2.5-14b-instruct
      api_base: http://REMOTE_IP:3009/v1
      api_key: "not-needed"

  # NAS Ollama models
  - model_name: nas/coder
    litellm_params:
      model: ollama/qwen2.5-coder:7b
      api_base: http://NAS_IP:11434

Key: llama.cpp endpoints need the openai/ prefix in model name and /v1 in api_base for LiteLLM compatibility.

Links

• Lumina Homelab: luminahomelab.ai
• X/Twitter: @HK47LUMINA
• GitHub: mlesnews