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Install Ollama to run the model, install Continue in VS Code, pull the exact model tag with Ollama, and then point a Continue model entry at that tag. Continue supplies the editor interface and coding workflows; Ollama downloads, loads, and serves the model. The integration is local by default, but Continue can also connect to an Ollama endpoint on another machine.
The details that most often determine whether the setup works are exact model naming, deliberate role assignment, available memory, and genuine tool support for Agent mode. A model entry in Continue does not download a model, and a tool_use capability flag cannot give an incompatible model capabilities it does not have.
What the Ollama–Continue setup does
This arrangement has two separate components:
- Ollama runs the language model and exposes an API, normally on the local service at
http://localhost:11434. - Continue is the VS Code extension that sends chat, edit, autocomplete, and other requests to that model.
VS Code itself is the host for Continue’s interface. When you ask a question in the Continue sidebar, request an edit, or accept an inline completion, Continue sends the prompt to the model configured for that role. Ollama returns the response. Keeping the model on your computer can reduce dependence on a hosted API, but model quality and speed still depend on the model, quantization, available memory, and your hardware.
The instructions below use the current configuration pattern documented by Continue. Its Ollama guide identifies Ollama v0.11.x and Continue v1.1.x as the basis for that guide; labels and configuration screens can change, so check the wording shown by the versions you install.
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Before you install: choose a model and deployment location
Pick a model that fits the job
A single model can serve several roles, but it does not have to. Chat and code editing benefit from a model that handles longer prompts and reasoning. Autocomplete is usually more responsive with a smaller model that can generate short continuations quickly. Continue’s model configuration supports roles such as chat, edit, apply, autocomplete, embed, and rerank.
Do not treat the model examples in older Ollama installation articles as current recommendations. For example, an Ollama post dated May 31, 2024 described Codestral 22B as needing substantial VRAM and, at that time, having a non-production license. That is historical context rather than current licensing advice. Check the model publisher’s present license and Ollama tag before adopting any model for work.
Decide where Ollama will run
| Deployment | Endpoint in Continue | Best fit | Important consideration |
|---|---|---|---|
| Same computer as VS Code | http://localhost:11434 (the usual local service) |
Private, simplest setup with no network hop | The computer must have enough memory for the model and its context. |
| Another computer you control | The reachable Ollama URL and port for that host | A workstation or server with more memory or a GPU | Routing, firewall rules, authentication, and transport security become your responsibility. |
Start locally unless you have a clear reason to use a remote host. A remote endpoint should be reachable only by trusted clients and should not be exposed directly to the public internet without appropriate network controls.
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- Install Ollama for your operating system. Use Ollama’s official installer, then start the Ollama service. On a desktop installation the service normally listens on port
11434. - Install Continue in VS Code. Open the Extensions view, search for Continue, and install the Continue extension. Continue also has an integration for JetBrains IDEs, but this guide uses VS Code.
- Restart or reload VS Code if requested. Open Continue’s sidebar after installation so its configuration editor or model-block workflow is available.
- Confirm the service before configuring the extension. If Ollama is not running, Continue cannot load a model even when the YAML is correct.
Ollama and Continue are independent installations. Installing Continue does not install Ollama, and installing an Ollama model does not add it to Continue automatically.
Pull and verify the Ollama model
Pull the model before adding it to Continue. Replace the example tag below with the exact tag you intend to use:
ollama pull <model-tag>Then list the models Ollama knows about:
ollama listCopy the model identifier exactly as Ollama displays it, including a version or quantization suffix when one is present. Continue sends that identifier to Ollama; it does not resolve an approximate name for you. A configuration that says model-name:latest can fail if you pulled an explicit tag such as model-name:some-version, and the reverse can also happen.
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You can inspect models currently loaded in memory with:
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ollama psThat command is useful after a request: it shows whether Ollama has loaded the expected model and helps distinguish a model-loading problem from a Continue connection problem.
Configure Continue to use Ollama
Use a model entry with the exact ID
Open Continue’s model configuration from the extension. Depending on the installed Continue version, this may be presented as a model-block editor or a YAML configuration file. Add an entry with a display name, the Ollama provider, the exact model ID from ollama list, and the local API base:
name: Local Ollama
version: 0.0.1
schema: v1
models:
- name: Local chat model
provider: ollama
model: <exact-model-id-from-ollama-list>
apiBase: http://localhost:11434
roles:
- chat
- editThe name shown to you can be any useful label; the model value cannot. Keep the provider as ollama. The apiBase line is appropriate when the Ollama service is on the same computer. If your Continue version supplies a local default, you may omit the line, but specifying it makes the intended endpoint clear.
Add autocomplete or other roles intentionally
To use the same model for inline completion, add autocomplete to its role list:
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roles:
- chat
- edit
- autocompleteThat is a convenience, not a performance guarantee. A model that is comfortable for conversational answers can still feel too slow for every keystroke. A separate, smaller model can be assigned only to autocomplete while a larger one handles chat and edits:
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models:
- name: Local chat model
provider: ollama
model: <chat-model-id>
apiBase: http://localhost:11434
roles:
- chat
- edit
- apply
- name: Local autocomplete model
provider: ollama
model: <autocomplete-model-id>
apiBase: http://localhost:11434
roles:
- autocompleteUse embed or rerank only when you have models that support those functions and a Continue workflow that needs them. Assigning a role does not transform a general model into a specialized one.
Connect to a remote Ollama host
For an Ollama service on another machine, replace the local API base with the endpoint that VS Code can reach:
apiBase: http://<ollama-host>:<port>Use the address and port actually configured on that host. Test network reachability from the computer running VS Code, and keep the service behind suitable firewall and access controls. The fact that an endpoint responds on a private network does not make it safe to publish publicly.
Test chat, editing, and autocomplete separately
Chat
- Open Continue’s sidebar in VS Code.
- Select the model entry assigned the
chatrole. - Ask a small, well-scoped question about the open file, such as requesting an explanation of one function.
- Confirm that the response arrives and that
ollama psshows the expected model.
Start with a short prompt. A large repository-wide request can consume context and memory before you know whether the basic connection works.
Edit and apply
Use a small, reversible change first: ask Continue to add input validation to one function, inspect the proposed diff, and apply it only after reviewing the result. The edit role generates an edit-oriented response; apply is a separate role in Continue configurations and may be assigned to a model that can reliably produce the required patch format.
Autocomplete
Open a source file, place the cursor where a completion is useful, and type a short function signature or comment. If no suggestion appears, verify that a model has the autocomplete role and that autocomplete is enabled in Continue’s settings. Test it independently of chat; a working chat model does not prove that inline completion is configured.
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Agent mode: tool support is a real model capability
Agent mode is more demanding than ordinary chat because the model must decide when to call tools and provide arguments in a format Continue can parse. Continue’s Agent documentation says it uses the Chat model when that model supports tool calling. Continue also documents system-message tools that encode tool calls in a structured format for Continue to interpret.
Some configurations advertise support with:
capabilities:
- tool_useThis setting is metadata. It can tell Continue to treat the model as tool-capable, but it cannot add tool calling to a model that does not actually support it. Manually declaring tool_use will not make an incompatible model operate as an agent.
When Agent mode fails
- Check the model’s documented capabilities rather than relying on the configuration flag.
- Try a model with native tool calling if one is available for your Ollama setup.
- If the model lacks native support, try Continue’s documented system-message-tool option when your installed version provides it.
- Reduce the task to a simple read-only request before allowing file edits or shell-related tools.
- Review the generated tool arguments and the proposed file changes; local execution does not remove the need for approval.
A model can produce excellent chat answers yet fail at structured tool calls. Treat Agent mode as a separate compatibility decision, not as an automatic feature of every Ollama model.
Memory, context length, and model size
Continue’s Ollama guide gives rough memory guidance, not guaranteed minimum hardware requirements:
| Model size cited by the guide | Approximate memory guidance | How to interpret it |
|---|---|---|
| 1.5B–3B parameters | About 4 GB RAM | Rough planning figure; actual use varies with quantization, context, and other processes. |
| 7B parameters | About 8 GB RAM | Not a guarantee that every 7B model will load or run comfortably. |
| 13B parameters | About 16 GB RAM | Leave additional memory for the operating system, VS Code, and context. |
| 32B parameters | About 32 GB RAM | Large models can need substantially more resources depending on how they are packaged and run. |
These figures are published by Continue as approximate guidance and are not the result of a detailed hardware or quantization test. Response speed also depends on CPU or GPU acceleration, memory bandwidth, quantization, prompt length, and whether another model is already loaded.
Lower context when memory is the problem
Continue may request a larger context than another Ollama client. Continue’s provider troubleshooting states: “Continue may set a higher default context length than other Ollama tools, causing this error even when the model works elsewhere.” If the model works in Ollama but fails through Continue, lower contextLength in the model’s defaultCompletionOptions:
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defaultCompletionOptions:
contextLength: 4096Use a value appropriate for your model and available memory. A smaller context can prevent out-of-memory errors, but it gives the model less surrounding code and conversation history. If reducing context is still insufficient, choose a smaller model and close other memory-heavy applications.
Troubleshooting by symptom
“Model not found”
- Run
ollama list. - Compare the displayed identifier character for character with Continue’s
modelvalue. - Pull the missing or differently tagged model with
ollama pull <exact-tag>. - Reload Continue’s configuration and try again.
Pay particular attention to an implicit latest tag versus an explicit version tag. Continue will not download a missing model simply because the YAML entry exists.
Out-of-memory or context errors
- Lower
contextLengthunderdefaultCompletionOptions. - Choose a smaller model or a less demanding quantization.
- Close applications that compete for RAM or VRAM.
- Use
ollama psto see which model is loaded and whether an unexpected model remains resident.
Continue cannot connect
- Verify that the Ollama service is running.
- For a local setup, confirm that port
11434is listening on the same computer. - For a remote setup, check the host name, port, routing, and firewall rules from the VS Code computer.
- Confirm that
apiBasehas no typo and points to the Ollama service rather than to a web page or unrelated API. - Do not expose the service broadly just to bypass a connection error; fix the network path and access controls instead.
Chat works but autocomplete does not
Check that the selected model has the autocomplete role and that Continue’s autocomplete feature is enabled. If the model responds only after a long delay, assign a smaller model to autocomplete rather than assuming the Ollama connection is broken.
Agent mode ignores tools or emits unusable calls
Remove the assumption that a capability declaration is sufficient. Verify native tool support, then try the documented system-message-tool approach if available. If neither works, use chat or edit mode for the task and apply changes manually.
Configuration choices and their trade-offs
| Decision | Option A | Option B | Choose based on |
|---|---|---|---|
| Model assignment | One model for chat, edit, and autocomplete | Separate chat/edit and autocomplete models | Whether simplicity or faster inline suggestions matters more on your hardware. |
| Configuration | Continue model block or autodetection | Explicit YAML entry | Autodetection is convenient; YAML gives precise model IDs, roles, endpoint, and options. |
| Deployment | Ollama on the VS Code computer | Remote Ollama endpoint | Local privacy and simplicity versus the resources available on another controlled host. |
| Agent behavior | Native model tool calling | Continue system-message tools | The model’s actual tool capability and what your Continue version supports. |
There is no universal best combination. Compare the choices by role support, exact tag availability, context and memory demand, response speed on your computer, and the security of the endpoint carrying your source code.
Keep the setup maintainable
- Record the exact model tags that work so a later pull does not silently switch to a different default.
- Keep chat/edit and autocomplete roles explicit, especially when several models are installed.
- Recheck Continue’s living provider and model documentation after upgrading the extension or Ollama; configuration keys and defaults can change.
- Review model licenses before using a model in commercial or production work. Historical blog guidance is not a substitute for the current license.
- When diagnosing a failure, change one variable at a time: first the model ID, then the endpoint, then context length or role assignment.
Once the basic chat request succeeds, add edits, autocomplete, and Agent mode one at a time. That sequence leaves you with a clear failure boundary and avoids mistaking a role or tool incompatibility for a broken Ollama installation.
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