llamactl/docs/managing-instances.md

Managing Instances

Learn how to effectively manage your llama.cpp, MLX, and vLLM instances with Llamactl through both the Web UI and API.

Overview

Llamactl provides two ways to manage instances:

• Web UI: Accessible at http://localhost:8080 with an intuitive dashboard
• REST API: Programmatic access for automation and integration

Authentication

If authentication is enabled:

1. Navigate to the web UI
2. Enter your credentials
3. Bearer token is stored for the session

Theme Support

• Switch between light and dark themes
• Setting is remembered across sessions

Instance Cards

Each instance is displayed as a card showing:

• Instance name
• Health status badge (unknown, ready, error, failed)
• Action buttons (start, stop, edit, logs, delete)

Create Instance

Via Web UI

1. Click the "Create Instance" button on the dashboard
2. Enter a unique Name for your instance (only required field)
3. Select Target Node: Choose which node to deploy the instance to from the dropdown
4. Choose Backend Type:
   • llama.cpp: For GGUF models using llama-server
   • MLX: For MLX-optimized models (macOS only)
   • vLLM: For distributed serving and high-throughput inference
5. Configure model source:
   • For llama.cpp: GGUF model path or HuggingFace repo
   • For MLX: MLX model path or identifier (e.g., mlx-community/Mistral-7B-Instruct-v0.3-4bit)
   • For vLLM: HuggingFace model identifier (e.g., microsoft/DialoGPT-medium)
6. Configure optional instance management settings:
   • Auto Restart: Automatically restart instance on failure
   • Max Restarts: Maximum number of restart attempts
   • Restart Delay: Delay in seconds between restart attempts
   • On Demand Start: Start instance when receiving a request to the OpenAI compatible endpoint
   • Idle Timeout: Minutes before stopping idle instance (set to 0 to disable)
   • Environment Variables: Set custom environment variables for the instance process
7. Configure backend-specific options:
   • llama.cpp: Threads, context size, GPU layers, port, etc.
   • MLX: Temperature, top-p, adapter path, Python environment, etc.
   • vLLM: Tensor parallel size, GPU memory utilization, quantization, etc.
8. Click "Create" to save the instance

Via API

# Create llama.cpp instance with local model file
curl -X POST http://localhost:8080/api/instances/my-llama-instance \
  -H "Content-Type: application/json" \
  -d '{
    "backend_type": "llama_cpp",
    "backend_options": {
      "model": "/path/to/model.gguf",
      "threads": 8,
      "ctx_size": 4096,
      "gpu_layers": 32
    }
  }'

# Create MLX instance (macOS only)
curl -X POST http://localhost:8080/api/instances/my-mlx-instance \
  -H "Content-Type: application/json" \
  -d '{
    "backend_type": "mlx_lm",
    "backend_options": {
      "model": "mlx-community/Mistral-7B-Instruct-v0.3-4bit",
      "temp": 0.7,
      "top_p": 0.9,
      "max_tokens": 2048
    },
    "auto_restart": true,
    "max_restarts": 3
  }'

# Create vLLM instance
curl -X POST http://localhost:8080/api/instances/my-vllm-instance \
  -H "Content-Type: application/json" \
  -d '{
    "backend_type": "vllm",
    "backend_options": {
      "model": "microsoft/DialoGPT-medium",
      "tensor_parallel_size": 2,
      "gpu_memory_utilization": 0.9
    },
    "auto_restart": true,
    "on_demand_start": true,
    "environment": {
      "CUDA_VISIBLE_DEVICES": "0,1",
      "NCCL_DEBUG": "INFO",
      "PYTHONPATH": "/custom/path"
    }
  }'

# Create llama.cpp instance with HuggingFace model
curl -X POST http://localhost:8080/api/instances/gemma-3-27b \
  -H "Content-Type: application/json" \
  -d '{
    "backend_type": "llama_cpp",
    "backend_options": {
      "hf_repo": "unsloth/gemma-3-27b-it-GGUF",
      "hf_file": "gemma-3-27b-it-GGUF.gguf",
      "gpu_layers": 32
    }
  }'

# Create instance on specific remote node
curl -X POST http://localhost:8080/api/instances/remote-llama \
  -H "Content-Type: application/json" \
  -d '{
    "backend_type": "llama_cpp",
    "backend_options": {
      "model": "/models/llama-7b.gguf",
      "gpu_layers": 32
    },
    "nodes": ["worker1"]
  }'

Start Instance

Via Web UI

1. Click the "Start" button on an instance card
2. Watch the status change to "Unknown"
3. Monitor progress in the logs
4. Instance status changes to "Ready" when ready

Via API

curl -X POST http://localhost:8080/api/instances/{name}/start

Stop Instance

Via Web UI

1. Click the "Stop" button on an instance card
2. Instance gracefully shuts down

Via API

curl -X POST http://localhost:8080/api/instances/{name}/stop

Edit Instance

Via Web UI

1. Click the "Edit" button on an instance card
2. Modify settings in the configuration dialog
3. Changes require instance restart to take effect
4. Click "Update & Restart" to apply changes

Via API Modify instance settings:

curl -X PUT http://localhost:8080/api/instances/{name} \
  -H "Content-Type: application/json" \
  -d '{
    "backend_options": {
      "threads": 8,
      "context_size": 4096
    }
  }'

!!! note Configuration changes require restarting the instance to take effect.

View Logs

Via Web UI

1. Click the "Logs" button on any instance card
2. Real-time log viewer opens

Via API Check instance status in real-time:

# Get instance details
curl http://localhost:8080/api/instances/{name}/logs

Delete Instance

Via Web UI

1. Click the "Delete" button on an instance card
2. Only stopped instances can be deleted
3. Confirm deletion in the dialog

Via API

curl -X DELETE http://localhost:8080/api/instances/{name}

Instance Proxy

Llamactl proxies all requests to the underlying backend instances (llama-server, MLX, or vLLM).

# Get instance details
curl http://localhost:8080/api/instances/{name}/proxy/

All backends provide OpenAI-compatible endpoints. Check the respective documentation:

• llama-server docs
• MLX-LM docs
• vLLM docs

Instance Health

Via Web UI

1. The health status badge is displayed on each instance card

Via API

Check the health status of your instances:

curl http://localhost:8080/api/instances/{name}/proxy/health