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A local AI coding assistant is a chain of four parts: your editor, an extension or provider, a model runtime, and a language model. The editor supplies files, cursor position and instructions; the extension sends that context to the runtime; the model generates code or explanations; and the response returns to the editor. When inference runs on your computer, the model is local, but that does not automatically make every editor-AI feature offline. Inline suggestions, semantic search, embeddings and agent services may use separate cloud components. In VS Code, the supported Ollama route uses the official extension, which discovers an Ollama server at http://127.0.0.1:11434 and exposes its available models in Chat.
What makes an AI coding assistant “local”?
“Local” describes where inference takes place: a model running on your own computer rather than on a provider’s remote server. It does not describe the entire editor, extension or account system. A single assistant can combine local chat with cloud-based indexing, telemetry, authentication, model downloads or specialized features.
The practical architecture has four connected layers:
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- Editor and workspace: VS Code or another editor displays the conversation, selection, diagnostics and generated edits. It determines which files, symbols or terminal output are available as context.
- Extension or provider: This is the bridge between the editor interface and a model service. It translates editor actions into prompts, sends context, receives responses and may implement tools such as file editing.
- Model runtime: A runtime such as Ollama loads model files, manages memory and exposes a local API. It can run on the same computer as the editor or on another machine on your network.
- Language model: The selected model predicts code, explanations or tool calls. Context length, vision, reasoning, tool calling and speed depend on that model and the runtime, not simply on the editor.
This separation explains why two extensions using the same model can behave differently: they may include different workspace context, support different tools or apply different safety and approval rules.
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How does VS Code connect to Ollama?
For Ollama models, install and start Ollama, make at least one model available, install the official Ollama VS Code extension, open Chat, and select a model under the Ollama section. The extension discovers a local Ollama server at http://127.0.0.1:11434 by default.
- Install Ollama and start its server. The server must be running before VS Code can enumerate models.
- Download or otherwise make a model available to Ollama. Choose a coding-capable model whose context window and hardware requirements fit your machine.
- Install the official Ollama extension for VS Code. VS Code documentation says its built-in Ollama provider is deprecated; use the extension instead.
- Open Chat in VS Code. Use the model picker and expand the Ollama section.
- Select the model and send a small request. Start with an explanation or short code change so you can confirm that the editor can reach the server before attempting a large agent task.
Local Ollama models do not require an Ollama sign-in. Cloud-hosted models exposed through Ollama follow a different account and connectivity path, so do not assume that choosing an Ollama-labelled model means inference is local.
Which VS Code features work with a local model?
Feature support is independent: local chat does not imply local autocomplete, search or every agent capability. VS Code’s BYOK local-model support can provide chat and, in supported configurations, agent workflows and utility tasks. Local BYOK models can be used offline for chat, while other features may invoke Copilot services.
| Capability | What to expect with a local BYOK model |
|---|---|
| Chat and explanations | Supported when the extension/provider can reach the local runtime; chat can work offline. |
| Agent workflows and tool use | Available in supported configurations. Tool calling depends on the selected model, and the extension controls which actions require approval. |
| Inline suggestions | VS Code currently does not make local BYOK models available for inline suggestions. |
| Semantic search and embeddings | May depend on GitHub Copilot services rather than the local model. |
| Vision, reasoning and long-context work | Varies by model and runtime; verify the model’s declared capabilities and context limit. |
Therefore, “offline coding assistant” is an accurate description only for the specific feature and configuration that has no required network dependency. An editor can show a local chat response while another command still contacts a service.
What is the difference between BYOK local models and Copilot-managed on-device models?
They are separate VS Code routes. BYOK means you configure a provider such as a local Ollama server yourself. Copilot-managed on-device models are an experimental Microsoft-managed path with its own account and hardware requirements.
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BYOK local models
- You choose the runtime, model and provider configuration.
- A GitHub sign-in or Copilot plan is not required for the local model itself.
- Chat can run completely offline once the runtime and model are present.
- Inline suggestions remain unavailable for local BYOK models in current VS Code documentation.
Experimental managed on-device models
- They require GitHub sign-in and supported hardware.
- The model and integration are managed through the Copilot-related route rather than your own Ollama server.
- Availability and requirements are experimental and can change.
Do not use documentation for one route to estimate the other route’s performance, privacy or hardware needs.
Does a local AI coding assistant work offline?
It can, but only after the model and runtime are already installed and for features that do not require a remote service. VS Code explicitly documents that a BYOK local model can be used completely offline for chat. Downloads, updates, cloud models, Copilot-dependent features and any extension action that calls an external service still need connectivity.
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- The runtime is running locally and the model is already downloaded.
- The extension points to the local server rather than a cloud endpoint.
- Your request uses chat or another feature documented to support local execution.
- Workspace indexing, embeddings, authentication and tool integrations are not configured to use a remote service.
Offline does not mean risk-free. A local agent can still read sensitive files and execute approved actions on your machine. Local models may also use different safety filters from Copilot cloud models.
How much hardware does local coding require?
There is no single hardware baseline. Model size, quantization, context length, concurrent requests and response speed determine memory and compute needs. Smaller models may run on ordinary developer hardware, while larger models can require substantial GPU memory.
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The clearest published requirement is for VS Code’s experimental MAI Code 1.1 Flash on-device route: an NVIDIA GPU with at least 96 GB of VRAM and approximately 55 GB of free disk space. Those figures apply to that model and route only; they are not minimum requirements for Ollama or every local coding model. The same setup also requires a compatible llama.cpp build.
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Choose hardware from the selected model’s actual memory requirement, not from a generic “AI-ready” label. VRAM must cover model weights plus runtime overhead and the context you intend to send. If a model spills to system memory, it may still run but with lower throughput.
What should I check before choosing a local model?
Start with the work you need rather than the model’s name. A compact model may be adequate for code explanation but weaker at multi-file refactoring; a larger model may improve reasoning while increasing latency and memory use.
- Context length: Confirm that the model can fit the files, diagnostics and instructions you expect to send in one request.
- Tool calling: If you want an agent to edit files or run commands, select a model and extension combination that supports tool calls.
- Reasoning: Models differ in planning and debugging ability; a coding label alone does not guarantee strong reasoning.
- Vision: Screenshots, diagrams or UI mockups require a vision-capable model.
- Performance: Generation speed depends on model size, quantization, CPU/GPU hardware and context length.
- License and distribution: Confirm that the model may be used for your project and that its files come from a source you trust.
How does data flow through a local editor assistant?
When you submit a prompt, the extension typically gathers your instruction plus selected code, open files, diagnostics or other workspace context. It sends that payload to the configured provider, receives tokens or a tool call, and renders the result. A tool-enabled request can create another cycle: the extension asks for approval, performs an action, then sends the result back to the model.
Inspect this flow before using proprietary code. Check which files are included automatically, whether terminal output is attached, where logs are stored, and whether semantic indexing or embeddings use a remote service. A local inference endpoint limits where the prompt is processed, but it does not prevent the editor or another feature from making separate network requests.
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How can I troubleshoot a local VS Code connection?
The Ollama section is missing
Confirm that the official extension is installed and that Ollama’s server is running. Restarting the extension after starting the server can force model discovery. Do not rely on the deprecated built-in provider.
No models appear
Make sure at least one model has been downloaded and is visible to the same Ollama installation that is serving http://127.0.0.1:11434. If multiple user accounts or installations exist, the editor may be reaching a server with a different model directory.
The model responds slowly or fails on long files
Reduce the amount of context, shorten the request or choose a smaller model. Check available system and GPU memory, because context expansion can exhaust memory even when the model loaded successfully.
An agent cannot perform an action
Verify tool-calling support in the model and extension configuration. Some models can generate code but cannot emit the structured tool calls an agent requires. Review approval settings before enabling actions that modify files or execute commands.
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Chat works offline but another feature does not
That behavior is expected when the second feature depends on Copilot services, embeddings or semantic search. Treat each capability as a separate network and privacy decision rather than diagnosing the whole editor as online or offline.
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When is a local assistant the right choice?
Use a local runtime when you need control over model selection, want offline chat, or have code that should remain within your machine during inference. Accept the setup and maintenance burden: you must install models, monitor memory, update runtimes and evaluate output quality yourself.
Use a cloud or Copilot-backed feature when you need inline suggestions, managed indexing, a model unavailable locally or a simpler setup. A hybrid workflow is often practical: local chat for repository questions and cloud services for features that currently have no local equivalent. Document which commands and files each route can access, and require confirmation for destructive agent actions.
Frequently Asked Questions
Can I use a local model without signing in to GitHub?
Yes, a BYOK Ollama model can be configured without a GitHub sign-in or Copilot plan. GitHub authentication is still required for Copilot-managed on-device models and for features that explicitly use Copilot services.
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Usually, the model only sees context supplied in the current request. Persistent project memory, indexes and conversation history are extension features; check where those artifacts are stored and whether they are rebuilt locally or by a remote service.
Can I run Ollama on another computer and connect VS Code to it?
The editor only needs a reachable Ollama API, so a separate machine can serve the model if the runtime is configured to accept that connection. This changes the privacy boundary: code leaves the editor computer and travels across your network, so secure the endpoint and restrict access.
Why does local autocomplete lag behind local chat?
Inline completion requires a rapid, continuous request path and a VS Code integration that supports local providers. Current VS Code documentation does not expose local BYOK models for inline suggestions, even though the same model can answer chat prompts.
Are local model safety filters identical to cloud Copilot filters?
No. VS Code warns that local models might not use the same safety filters as Copilot cloud models. Treat generated commands as untrusted, review proposed edits and keep approval gates enabled for agents.
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