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Fix a local coding assistant by testing outward from the model runtime, not by repeatedly changing settings in the extension. First confirm the runtime is running, then query its model-list endpoint directly. Verify that the exact model identifier is installed or loaded, and only then correct the assistant’s provider, base URL, API path, and model field. If Docker, WSL, or another computer is involved, remember that localhost may refer to the wrong network namespace. Finally, separate TLS, proxy, CORS, GPU, memory, and permission failures by checking logs and retry with a minimal request.
This order tells you whether the failure is a dead server, an unreachable server, a missing model, an incompatible API path, or a request that reaches the server but cannot run.
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What does “cannot connect to model” actually mean?
An assistant’s connection error is only a client-side symptom. The model service might be stopped, listening on another address, reachable but missing the requested model, rejecting an incompatible URL, or running out of resources after accepting the request.
| Observed symptom | Most likely layer | First evidence to collect |
|---|---|---|
| Connection refused or timeout | Process, port, network namespace, or firewall | Open the runtime’s base address and query its model endpoint from the same environment as the assistant |
| HTTP response but “model not found” | Model installation, loading, or identifier mismatch | List models and compare the returned ID character-for-character with the assistant configuration |
| 404 or method/path error | Wrong API contract | Check whether the provider requires /v1 or Docker Model Runner’s /engines/v1 |
| TLS certificate or chain error | CA trust or proxy interception | Inspect the certificate chain and configure the correct CA bundle rather than disabling verification |
| Request starts, then stalls or crashes | RAM, VRAM, context size, permissions, or GPU startup | Read server logs while sending a short request |
Do not treat a green extension status as proof that inference works. The decisive test is a direct, successful model-list request followed by a tiny completion.
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How do I identify where the assistant and model server are running?
Write down the execution boundary before changing a URL. A desktop editor and a local server on the same operating-system installation can normally use localhost; a container, WSL distribution, or remote host changes what that name resolves to.
- Same host: the editor and runtime share the host network. Use the runtime’s documented loopback address and port.
- Editor in Docker, server on the host: container loopback points back to the container. Use the host gateway address supported by your platform, often
host.docker.internal, and allow the port through the host firewall. - Server in Docker, editor on the host: publish the container port and connect to the published host port, not an internal container address.
- WSL and Windows split: determine which side owns the listening socket. WSL networking mode and firewall policy determine whether Windows
localhostreaches a WSL service and vice versa. - Remote server: bind the runtime to an address reachable from the client, restrict access with firewall rules, and use the remote machine’s LAN or VPN address instead of loopback.
Record the machine, namespace, interface, port, and protocol for both processes. This prevents a correct server from being “fixed” with an incorrect client URL.
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Start the server using its supported control, then test its base address from the same environment as the assistant. A browser request can establish basic reachability; curl gives a reproducible status code and response body.
Ollama
- Start the service with
ollama serveif it is not already managed by the operating system. - Test the OpenAI-compatible base at
http://localhost:11434/v1. The local alias does not require an API key. - Query the provider’s model-list route, typically
http://localhost:11434/v1/models, from the assistant’s network namespace.
Continue’s troubleshooting guidance specifically recommends checking http://localhost:11434, using ollama serve, and verifying the model configuration. A refusal means you should stay at the process or networking layer; changing the model name cannot repair a closed port.
LM Studio
- Open the Developer tab and enable Start server, or run
lms server start. - Confirm the server’s displayed host and port. The common local address documented for assistant setup is
http://localhost:1234. - Use the server’s OpenAI-compatible model-list route, normally under its displayed
/v1API path, and confirm that the response is JSON rather than an HTML error page.
LM Studio describes its server as usable on localhost or across a network. If the Developer toggle is off, no client setting can discover a model.
Docker Model Runner
- Confirm Docker Model Runner is enabled and the model service is running.
- For Cline, set the base URL to
http://localhost:12434/engines/v1. - Keep
/engines/v1in the URL and omit a trailing slash. Verify the endpoint withcurl http://localhost:12434/engines/v1/models.
A response from the wrong path can look like a networking failure in the extension. Docker’s IDE integration also calls out service status and CORS checks, so inspect both when the endpoint is reachable but the browser-based client is rejected.
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How do I check that the requested model is installed and loaded?
A reachable API can still reject inference when the model is absent, unloaded, or named differently. Treat the model-list response as the source of truth.
- Save the exact model ID returned by the server, including repository, size, quantization, revision, and punctuation.
- Compare it with the assistant’s model field. Do not substitute a friendly display name for the identifier expected by the API.
- If it is absent, pull or download it in the runtime, or load it in the runtime’s model-management interface.
- Retry the model-list request and then select the newly returned ID in the assistant.
Different providers may expose a tag such as a quantized variant rather than the name shown in a catalog. Copying the ID from the API response avoids silent mismatches. Docker’s local-model documentation distinguishes a missing-model response from connection refusal; handle those as separate problems.
Which base URL and API path should I enter?
Use the provider’s documented base URL exactly. The path is part of the API contract: adding or removing /v1 or /engines/v1 by guesswork commonly produces 404, authentication, or empty-model errors.
| Runtime | Documented local address | Path detail | Model test |
|---|---|---|---|
| Ollama through an OpenAI-compatible client | http://localhost:11434/v1 |
Use the /v1 base; no API key is required for this local alias |
curl http://localhost:11434/v1/models |
| LM Studio | http://localhost:1234 (local setup) |
Use the API path shown by the Developer server, commonly its OpenAI-compatible /v1 route |
Query the displayed model-list route |
| Docker Model Runner with Cline | http://localhost:12434/engines/v1 |
Retain /engines/v1; no trailing slash |
curl http://localhost:12434/engines/v1/models |
In a client that asks separately for “provider,” “base URL,” and “model,” choose the provider matching the server’s protocol, paste only the base URL expected by that provider, and enter the exact returned model ID. Do not paste a complete /models URL into a field that expects a base URL.
Why does localhost fail in Docker, WSL, or a remote setup?
localhost always means “this network namespace.” It does not automatically mean your physical computer or another machine.
Docker
When the assistant runs in a container, replace localhost with the host gateway address available to that container, commonly host.docker.internal. When the server runs in a container, publish its port to the host and use that published port. Confirm the route from inside the assistant container, not only from a browser on the host.
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WSL
Check which interface the runtime binds to and whether your WSL networking mode exposes it to Windows or to another WSL distribution. If a service binds only to a loopback interface inside one environment, a client outside that environment may need the WSL address or a host-side forwarding rule. Continue’s troubleshooting guidance also recommends checking firewall policy and model installation for WSL configurations.
Another computer
Bind the runtime to a reachable interface, use the server machine’s LAN or VPN address, and permit only the necessary port in the firewall. Keep authentication and network segmentation in mind: a local API that has no key requirement should not be exposed broadly to an untrusted network.
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Test progressively: first from the server machine, then from the assistant’s container or WSL environment, and finally through the extension. The first location where the request fails identifies the boundary to repair.
What should I do about firewalls, CORS, proxies, and TLS?
These failures occur after basic process checks and require different fixes.
- Firewall: allow the listening port on the interface you intentionally expose. A service bound to loopback does not need a LAN firewall exception, while a remote client does.
- CORS: browser-based IDE integrations can reach the port but be blocked by cross-origin policy. For Docker Model Runner with Cline, check the runtime’s CORS configuration and status in addition to the URL.
- Proxy: VS Code or the operating system may route requests through a corporate proxy. Review the editor’s proxy settings and proxy bypass list so private hostnames and addresses are handled as intended.
- TLS: For
CERT_UNTRUSTED, self-signed, or chain-verification errors, install or point the client to the correct certificate authority bundle. Disabling certificate verification hides the cause and weakens the connection.
A successful TCP connection does not prove that the certificate is trusted or that a browser is permitted to make the request. Capture the exact error text and status code before changing security settings.
How do I read logs and diagnose GPU, memory, and permission failures?
Inspect the server while reproducing one failed request. Logs reveal startup, model-loading, GPU, file-permission, and out-of-memory evidence that an assistant’s generic error cannot show.
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| Deployment | Useful log command or location |
|---|---|
| Ollama on macOS | cat ~/.ollama/logs/server.log |
| Ollama under Linux systemd | journalctl -u ollama --no-pager |
| Ollama in a container | docker logs <container-name> |
| Ollama extra diagnostics | Restart with OLLAMA_DEBUG=1 when supported by your installation |
Look for a model-load failure, denied access to a model directory, an unavailable GPU backend, a CUDA or driver error, or an allocation that exceeds available RAM or VRAM. Fix the underlying runtime issue before changing the assistant provider.
Use realistic hardware expectations
Cline’s local-model guidance gives indicative, not guaranteed, memory bands: 16–32 GB of RAM for small quantized models, 32–64 GB for mid-size models, and 64 GB or more for larger models or wider context windows. Actual use varies with quantization, context length, concurrent tools, and whether layers run on the GPU. Docker documentation illustrates context-size choices of 4,096 and 128K tokens; a larger context can materially increase memory demand.
If a short prompt succeeds but a long agent task crashes, reduce context length, choose a smaller or more heavily quantized model, lower concurrency, or move more work to available GPU memory. Do not interpret an out-of-memory termination as a URL problem.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I retest after making a change?
Use a controlled sequence so one fix does not conceal another.
- Restart or confirm the runtime and verify its model-list endpoint.
- Copy one exact model ID from that response into the assistant configuration.
- Send a minimal completion request with a short prompt and no tools, long context, or agent orchestration.
- Check the server log for the request and its completion status.
- Only after that succeeds, restore your normal context length, tools, and agent settings one at a time.
If the model-list call fails, remain focused on process, URL, namespace, firewall, proxy, or TLS evidence. If it succeeds but the tiny completion fails, investigate model loading, API compatibility, permissions, and resources. If the tiny completion works, reintroduce advanced features until the first failing feature is isolated.
What should I configure in Cline or Continue?
Both clients follow the same diagnostic logic: start the runtime, select the matching provider, choose a local model, and verify the server independently before testing an agent task.
Cline
- Choose the provider that matches Ollama, LM Studio, or Docker Model Runner.
- Enter the documented base URL: Ollama commonly uses
http://localhost:11434for its local integration, LM Studio useshttp://localhost:1234, and Docker Model Runner useshttp://localhost:12434/engines/v1. - For Docker Model Runner, preserve
/engines/v1and remove a trailing slash. - Select a model that appears in the runtime’s model list, then run a short prompt.
If Cline reports no models, test the corresponding endpoint directly and check Docker status and CORS before editing the model field.
Continue
- Set the provider and base URL to the runtime’s documented API contract.
- For Ollama, check
http://localhost:11434, make sureollama serveis running, and verify the model named in the configuration is installed. - If the server is in Docker, WSL, or on another host, replace loopback with a reachable address and check firewall rules.
- For certificate errors, configure the required CA bundle and review VS Code proxy settings.
Continue’s “Unable to connect to local Ollama instance” message can therefore represent a stopped service, an unreachable namespace, or an incorrect model entry; the direct endpoint test distinguishes them.
Which failure pattern points to which fix?
| Pattern | Interpretation | Targeted action |
|---|---|---|
ECONNREFUSED immediately |
Nothing is listening at that address, or the address is wrong in this namespace | Start the server; verify host, port, container publishing, and WSL boundary |
| Timeout from a remote client | Routing or firewall blocks the listener | Test the server locally, bind to the intended interface, and permit the port |
404 on /models |
Wrong API path or provider mode | Use the provider’s exact base and path, including required version segments |
| Model-not-found response | Model is not installed, loaded, or named exactly | List models, pull or load the missing one, and copy its returned ID |
| Browser CORS error | Cross-origin policy blocks an otherwise reachable service | Review runtime CORS settings and the IDE’s origin requirements |
| Certificate-chain failure | Client does not trust the server or proxy certificate | Install/configure the correct CA bundle and inspect proxy interception |
| Works for tiny prompts, fails for long tasks | Context, RAM, VRAM, or concurrency limit | Reduce context or model size, lower concurrency, and inspect logs |
What should you record before escalating?
A concise diagnostic bundle makes support useful instead of speculative. Record the operating system, runtime and version, client extension and version, whether either side runs in Docker or WSL, the exact base URL (without secrets), the model ID returned by the server, the HTTP status and error text, and the relevant server-log lines from one reproduction. Include whether a direct model-list request and a minimal completion succeeded. Redact tokens, private IP details, and file paths that reveal sensitive information.
Frequently Asked Questions
Can a local model server work in a browser but fail in the coding assistant?
Yes. The browser may run on the host while the assistant runs in a container or WSL, so they resolve different loopback addresses. Test the model-list endpoint from the assistant’s own process environment and compare the resulting status and body.
Why does the model appear in the server UI but not in my assistant?
The UI may show a friendly label while the API exposes a tagged or quantized identifier. Copy the exact ID returned by the API, and ensure the client is using the same provider mode and API version as the server.
Do local Ollama connections need an API key?
The OpenAI-compatible local alias documented for Ollama does not require an API key. If a client insists on one, leave the field empty when allowed or use the provider’s local-auth setting rather than inventing a token.
Why does increasing context make connection errors return?
A larger context increases memory and compute requirements even after networking is correct. Compare a short completion with the failing task, then inspect runtime logs and reduce context, model size, or concurrent tool work.
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