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When a local AI coding assistant cannot connect, troubleshoot from the model outward: prove that the runtime is running, query its model-list endpoint directly, confirm the exact model identifier, then check the assistant’s provider, base URL and API path. If the assistant and server run in Docker, WSL or on different machines, localhost may refer to the wrong network namespace. A reachable server can still fail because a model is missing, unloaded, misnamed, blocked by TLS or a firewall, or too large for available resources.
What does a local-model connection error actually mean?
The message in your coding assistant identifies only the layer that reported failure. “Connection refused” usually means no process is listening at the address the client used, while “model not found” means the server answered but cannot resolve the requested model. A timeout or very slow first response can indicate model loading, insufficient memory, GPU problems or an oversized context rather than a dead server.
Use this order so each test removes a different uncertainty:
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- Identify where the assistant and model server execute: the same operating-system host, WSL, a Docker container or another machine.
- Start the runtime and call its HTTP endpoint directly, outside the assistant.
- Compare the model ID returned by the server with the ID configured in the assistant.
- Correct the provider selection, base URL and API path using that provider’s documented contract.
- Fix namespace, listening-interface, firewall, TLS, proxy and CORS issues.
- Inspect runtime logs and resource usage, then retry with a minimal completion request.
How do I identify the runtime and network boundary?
Write down the exact location of both processes before changing settings. On one host, localhost normally reaches the local listener. In WSL, Docker or a remote setup, it can point to the guest, container or remote machine instead of the computer running the model.
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Same computer and same operating system
Start the server and use its documented loopback address. Cline’s local-model guidance lists Ollama at http://localhost:11434 and LM Studio at http://localhost:1234. A browser or curl request from that same host is the fastest way to separate server failure from client configuration.
Docker containers
A client inside a container has its own network namespace. The host’s localhost is not automatically the container’s host. Use the host gateway or a reachable LAN address appropriate to your Docker configuration, allow the listening port through the host firewall, and check CORS when a browser-based integration is involved. Continue’s documentation specifically calls out Docker’s host.docker.internal as a possible host address.
WSL and remote machines
WSL networking can make a service bound only to one interface unreachable from the other environment. Bind the runtime to an interface that the client can reach, use that address in the assistant, and verify the port from the client’s own environment. For a server on another computer, use its LAN or routed address rather than loopback and restrict firewall access to trusted networks.
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Start the runtime before touching the assistant configuration. LM Studio can serve a model from its Developer tab by enabling Start server, or with lms server start. Ollama can be started with ollama serve. Docker Model Runner must be running before its OpenAI-compatible endpoint will answer.
Then call the model-list endpoint directly. Use the provider’s documented base URL and append its model path; do not guess a different port or path. Examples for OpenAI-compatible local servers are:
curl http://localhost:11434/v1/modelsfor an Ollama server using its OpenAI-compatible base URL.curl http://localhost:1234/v1/modelsfor an LM Studio server configured for its local API.curl http://localhost:12434/engines/v1/modelsfor Docker Model Runner.
Docker’s IDE integration requires the base URL http://localhost:12434/engines/v1. Keep /engines/v1 in that value and omit a trailing slash. A successful response proves that the address, port, route and basic HTTP access work; it does not prove that the requested model is installed or that the assistant is using the same API format.
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What are the documented startup methods, addresses and API paths?
The following values are the practical anchors for the three common local runtimes covered by the vendor guidance.
| Runtime or integration | Start method | Default address | Assistant base URL or model test |
|---|---|---|---|
| Ollama | ollama serve |
http://localhost:11434 |
OpenAI-compatible base URL: http://localhost:11434/v1; query the corresponding /models route. |
| LM Studio | Developer tab: Start server, or lms server start |
http://localhost:1234 |
Use the server’s documented API path and query its /models route. |
| Docker Model Runner with Cline | Start Docker Model Runner | http://localhost:12434 |
Base URL: http://localhost:12434/engines/v1; test with curl http://localhost:12434/engines/v1/models. |
Ollama’s OpenAI-compatible alias does not require an API key according to Docker’s local-model guidance. Do not add an authentication header merely because the assistant’s provider form contains an API-key field.
Why does the endpoint work but the assistant still show no model?
A healthy model-list response means the server is reachable, but the client can still request an identifier that is absent, unloaded or spelled differently. Copy the model ID exactly as returned by the server into the assistant’s model field; display names and tags are not necessarily interchangeable.
Check installation and loading state
Pull or download the model if the server’s list does not contain it. In LM Studio, load the intended model before sending a request. In Ollama, make sure the model has been pulled into the local runtime. If the server lists several variants, select the exact variant that the client will send, including any tag shown by the runtime.
Distinguish name errors from server errors
A model-not-found response after a successful /models call points to installation, loading or identifier configuration. A connection-refused response occurs earlier, before the server can inspect the model name. Fix the earlier layer first instead of repeatedly changing the model field.
How should I set the provider, base URL and API path?
Select the provider that matches the runtime, then enter its documented base URL exactly. The path is part of the API contract: Ollama’s OpenAI-compatible mode uses /v1, while Docker Model Runner’s Cline setup uses /engines/v1 without a final slash.
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- Open the assistant’s provider or model settings.
- Choose the matching local provider rather than a similarly named cloud provider.
- Paste the base URL, preserving the required path segment and port.
- Remove an accidental duplicate path such as
/v1/v1or a trailing slash where the integration forbids one. - Enter the exact model ID returned by the runtime.
- Leave the API-key field empty when the selected local endpoint does not require one.
- Save the configuration and send a short completion request before re-enabling tools, long context or agent features.
Do not add or remove /v1 or /engines/v1 by trial and error. A server can be online while every request fails because the client is targeting a valid host with the wrong route.
What should I change when localhost is unreachable from Docker, WSL or another machine?
Test from the same execution boundary as the assistant. A request that succeeds on the host but fails inside the container or WSL shell proves that the remaining problem is routing, binding or firewall policy rather than model installation.
Make the listener reachable
Configure the runtime to listen on an interface visible to the client instead of loopback-only binding when the client is remote. Use the resulting host or LAN address in the assistant. Continue’s documentation also covers binding Ollama to a remote interface and checking the firewall.
Check the firewall and port exposure
Allow only the required port and trusted source network. For Docker, confirm that the port is published or that the container can reach the host gateway. For WSL, verify access in both directions because the Windows host and Linux environment can have different addresses and firewall rules.
Check browser CORS behavior
When the request originates in a browser-based IDE integration, the server may be reachable but reject the browser’s cross-origin request. Docker’s IDE guidance calls for checking CORS as well as Docker Model Runner status. A command-line curl success therefore does not by itself prove that a browser client is permitted.
How do I fix TLS certificate and proxy failures?
If the client reaches the server but reports CERT_UNTRUSTED, a self-signed certificate or a chain-verification error, install or select the correct CA bundle for the client environment. Do not treat certificate validation as a connection problem and do not solve it by blindly disabling verification.
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When your network requires a proxy, review the proxy settings used by the IDE or editor, including VS Code’s proxy configuration referenced in Continue’s troubleshooting guidance. Ensure the proxy can reach the local or remote model address and is not rewriting the API path. Retest the model-list request and then a minimal completion after changing trust or proxy settings.
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Once the endpoint and model ID are correct, inspect the runtime logs at the time of a fresh request. Ollama documents these locations:
- macOS:
cat ~/.ollama/logs/server.log - Linux systems using systemd:
journalctl -u ollama --no-pager - Container deployment:
docker logs <container-name>
Set OLLAMA_DEBUG=1 when Ollama’s normal log output does not explain the failure. Look for startup crashes, permission denials, GPU initialization errors, out-of-memory messages and model-load failures. Capture the timestamp of one failed request so the relevant log lines are easy to isolate.
Use hardware guidance as a diagnostic, not a guarantee
Cline’s local-model guide gives indicative RAM bands: 16–32 GB for small quantized models, 32–64 GB for mid-size models, and 64 GB or more for larger models or wider context windows. These are setup guidelines, not performance guarantees. Docker’s examples include context sizes of 4,096 and 128K tokens; the larger context can require substantially more memory and may turn an apparently connected server into a slow or failing one.
If logs show memory pressure, first load a smaller or more heavily quantized model and reduce context. If the runtime cannot initialize the GPU, confirm that the process has permission to access it and retry on the CPU only if the runtime supports that configuration.
What does each common symptom point to?
| Symptom | Most likely layer | Evidence to collect | Corrective action |
|---|---|---|---|
| Connection refused or cannot open socket | Server stopped, wrong host or port, binding or firewall | Direct model-list request from the assistant’s environment | Start the server, correct the reachable address, expose the port or adjust firewall rules. |
| HTTP response but model not found | Missing, unloaded or misnamed model | Model IDs returned by the server | Install or load the model and copy its exact ID into the assistant. |
| Works on host, fails in container or WSL | Network namespace or interface binding | Request results from both environments | Use the host gateway or reachable LAN address and bind the service appropriately. |
| Browser integration rejected while curl works | CORS or browser proxy policy | Browser developer-console error and server CORS configuration | Allow the client origin as appropriate and review IDE proxy settings. |
TLS chain or CERT_UNTRUSTED error |
CA trust configuration | Client certificate error and server certificate chain | Configure the correct CA bundle and retest. |
| Server responds only after a long delay or crashes during generation | Model loading, GPU, RAM or context size | Runtime logs, memory use and request context | Reduce model or context requirements, fix permissions or GPU setup, then retry. |
What is the safest final retest?
After each correction, repeat the same small sequence rather than immediately restoring a complex agent workflow:
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- Call the model-list endpoint from the assistant’s execution environment.
- Confirm that the configured model ID appears in the response.
- Send a short completion with a tiny prompt and no tools or large files.
- Check the runtime log for one successful request and any warning.
- Re-enable tools, repository context and larger prompts one feature at a time.
This progression tells you exactly which added feature reintroduces a failure. Keep the known-good base URL and model ID unchanged while testing those features.
Frequently Asked Questions
Can a local model server be healthy even when the coding assistant reports an error?
Yes. The assistant may be using a different host, path or model identifier than the one you tested. A direct model-list response confirms reachability only; compare the returned ID and the exact request URL used by the client.
Why does the first request take much longer than later requests?
The runtime may be loading weights into RAM or GPU memory before generating text. Check the server log and memory usage, then reduce context or choose a smaller quantized model if loading repeatedly fails.
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No. A certificate error means the client does not trust the presented chain. Install or select the correct CA bundle and keep verification enabled, especially when the endpoint is reachable from other machines.
What should I record before asking for help?
Record the runtime, where each process runs, the exact base URL, the model ID returned by the server, the client error text and the relevant timestamped log lines. This lets someone distinguish routing, API compatibility, model state and resource failures without guessing.
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