Model Deployment | Kamiwaza Docs

Model Deployment Process

The model deployment process in Kamiwaza is designed to be simple and robust.

1. Initiate Deployment: When you request to deploy a model, Kamiwaza's Models Service takes over.
2. Engine Selection: The platform automatically determines the best engine based on your hardware, operating system, and the model's file format. For example, on a Mac with an M2 chip, a .gguf file will be deployed with llama.cpp, while .safetensors will use MLX. You can also override this and specify an engine manually.
3. Resource Allocation: The system allocates the compute resources needed for the selected runtime and configures the model's public route. Where the model lands — which node, which GPU, and how much memory is reserved — is decided by model placement; see the Model Placement Overview.
4. Launch: The selected engine is started in the environment appropriate for that deployment. In current Kubernetes deployments, this is typically a managed runtime workload exposed through the platform gateway.
5. Health Check: Kamiwaza monitors the model until it is healthy and ready to serve traffic.

Once deployed, your model is available via a standard API endpoint.

Deployment Lifecycle Statuses

Below are the deployment and instance statuses you may see, with what they mean and what (if anything) you should do.

• REQUESTED: The deployment request was accepted and recorded.
• DEPLOYING: Kamiwaza is creating the Ray Serve app (if applicable) and preparing routing.
• INITIALIZING: Routing is up and the model server is reachable, but the model is still loading or not yet ready. Normal for a short period right after launch.
• DEPLOYED: The deployment is healthy and ready to serve traffic.
• STOPPED: The deployment was stopped (either by a user action or system shutdown).
• ERROR: A recoverable problem was detected. Often resolves after a change or retry. See error code guidance below.
• FAILED: A terminal failure was detected (e.g., out-of-memory). Requires user action to resolve.
• MUST_REDOWNLOAD: Required weights are missing locally in community installs. Re-download the model and deploy again.

Instance-level Statuses (for replicas)

• REQUESTED: An instance record was created and is queued to start.
• COPYING_FILES: Required files are being synced to the node.
• DEPLOYED (instance): The process is launching or up and responding.

Error Codes and What to Do

If a deployment shows ERROR or FAILED, the UI may show a short error code and message. Common codes:

• OOM (Out of Memory): Reduce context size, select a smaller model/variant, or lower GPU memory utilization.
• CUDA_ERROR: Check GPU drivers/availability; restart GPU services or the host if needed; ensure the container has GPU access.
• MODEL_LOADING_FAILURE: Verify that all model files exist, are accessible, and match the expected version; try re-downloading.
• CONTAINER_EXITED: The runtime process crashed. Open logs for details; check memory limits, incompatible flags, or driver issues.
• RUNTIME_ERROR: A generic runtime exception was seen in logs. Open logs for specifics.
• STARTUP_TIMEOUT: The model did not become ready within the expected time. Try a smaller model/context or adjust engine parameters.
• MUST_REDOWNLOAD: Files missing locally (community installs). Re-download the model and retry.

Viewing Logs and Diagnostics

• In the advanced UI, open a deployment row and click “View logs” to see container logs and auto-detected issue patterns (OOM, CUDA errors, etc.).

Tips for Novice Mode

• If you hit OOM or STARTUP_TIMEOUT, try:
  • Selecting a smaller preset (model/variant)
  • Reducing context size (the UI will suggest balanced options)
  • Re-deploying after downloads complete

When to Retry vs. Change Configuration

• Retry directly if you see transient ERROR without an error code.
• Change configuration if you see a clear code like OOM, MODEL_LOADING_FAILURE, CONTAINER_EXITED, or STARTUP_TIMEOUT.

How Routing Works

Current Kamiwaza deployments use path-based routing as the standard public access model. Routes can be created immediately after launch while the runtime continues its readiness checks. This is why you may see INITIALIZING briefly before DEPLOYED.

Deployments are exposed on a single host using path prefixes. OpenAI-compatible base URL:

https://<host>/runtime/models/<deployment_id>/v1

Some older environments may still expose legacy port-based routes, but path-based URLs are the canonical pattern for current customer-facing documentation. See Routing & URLs for details.