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You can ask an AI model to explain or refactor code in VS Code without sending that code to a hosted model: use VS Code chat with a locally hosted model through Ollama. The local chat path can work without an internet connection, but it does not make every VS Code AI feature available offline. Your privacy boundary depends on which model and editor features you use.
What “offline” protects—and what it does not
With a local model, inference runs on your device rather than on a provider’s cloud service. Ollama’s Privacy Policy, last updated March 2026, says it does not collect, store, transmit, or access prompts and responses processed locally. That statement applies to content handled locally; it is not an independent security audit of your entire computer, VS Code installation, or every extension.
Ollama distinguishes local use from its cloud-hosted models: cloud prompts and responses are processed transiently to provide the service. Its policy also describes limited device and usage metadata collection, which it says does not include prompt or response content. Avoid treating “local model” as a blanket guarantee that no network traffic occurs.
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| Operating mode | Connectivity | Code handling | Editor capability and trade-offs |
|---|---|---|---|
| Local model in VS Code chat | VS Code says local-model chat can work without internet access. | Ollama says it cannot access prompts and responses processed locally. | Requires a local model and device resources. The cited documentation does not establish minimum hardware or comparative refactoring quality. Some VS Code service-dependent features are unavailable offline. |
| Cloud-hosted model or service feature | Requires network access for the relevant service. | Ollama says cloud-hosted prompts and responses are processed transiently; privacy terms depend on the provider and feature. | May provide service features unavailable through the documented offline path. The sources do not establish a general quality ranking. |
VS Code specifically lists semantic search, inline suggestions, and embedding-dependent features among the capabilities that require GitHub service connectivity and are unavailable through its BYOK offline path. A locally running model therefore does not mean every AI feature in the editor is local.
What you need to use Ollama in VS Code
Ollama’s VS Code integration documentation, retrieved October 7, 2026, lists Visual Studio Code 1.127 or newer, an installed and running Ollama service, and at least one model available in Ollama. The extension discovers Ollama at http://127.0.0.1:11434 by default. The documentation says local models do not require sign-in.
Ollama recommends version 0.17.6 or newer for cloud sign-in and richer model metadata; older versions may still work with local models. The same integration page says earlier VS Code versions do not reliably cancel provider requests. Requirements and integration details can change, so consult the linked official documentation if your installed versions or menus differ.
VS Code’s language-model documentation currently describes its built-in Ollama provider as deprecated and directs users to the official Ollama extension. Use that extension for the documented setup rather than relying on the deprecated provider.
Set up a local model and start a chat
- Install the prerequisites. Install VS Code 1.127 or newer and Ollama from their official sources, then start Ollama.
- Download a model while connected, if needed. In a terminal, run
ollama pull qwen3.6, the example command in Ollama’s integration documentation. This downloads the model; it does not establish that this model is best for refactoring. Check the model name and tag on the official page if the command is no longer current. - Install the official Ollama extension. In VS Code, open Extensions, search for Ollama, and install the official integration.
- Open VS Code chat and select the local model. Choose an available Ollama model through the chat or language-model interface, then ask a small, specific question about code. You do not need to sign in to use a local model, according to the integration documentation.
- Disconnect only if the task permits it. Local chat can work without internet, but service-dependent features such as semantic search and inline suggestions will not be available through the documented offline path.
VS Code’s language-model documentation explains the local-model chat route and offline feature boundaries: AI language models in VS Code. Ollama’s setup requirements and example command are in its VS Code integration documentation.
Rank #3
- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
Ask for a bounded refactor, then verify it
Keep the first request narrow: one function or a small, related set of files. Ask the model to explain the current behavior, propose a change, and state its rationale before applying the edit. Narrow scope makes the proposed change easier to inspect; it does not guarantee correctness.
- Ask for a brief explanation of the code’s current behavior and any assumptions the model is making.
- Request a proposed refactor limited to the specified function or files. Ask it not to change behavior unless you explicitly request that.
- Review the full diff yourself. Check the changed logic, error handling, public interfaces, and any assumptions about inputs or side effects.
- Run the repository’s existing tests, formatter, and static checks as applicable. Fix failures yourself or use another carefully scoped iteration, then inspect the new diff.
The official setup and privacy pages document how the integration works; they do not prove that a particular model produces correct refactors or establish which model is best for code changes. Treat generated edits as proposals, not verified patches.
Rank #4
- 【AI Max+ 395 AI Workstation】16 cores, 32 threads, up to 5.1 GHz boost and 80 MB cache. Integrated Radeon 8060S graphics with 40 CUs, RDNA 3.5, delivers performance close to RTX 4060/4070 laptop GPUs. Triple-engine design(CPU+GPU+XDNA 2 NPU) with up to 126 TOPS total, including 50+ TOPS dedicated NPU for local AI inference and machine learning acceleration. Ideal for AI development, content creation, virtualization, data analysis, and demanding multitasking. Compact, high-performance workstation.
- 【256-bit LPDDR5X MAX 128GB】The LPDDR5X onboard memory reaches 8400 MT/s - 1.5x faster than DDR5 SODIMM. Unlock the full potential of your graphics with massive 128GB memory pooling. This system allows you to manually assign up to 128GB of the onboard RAM to serve as video memory (VRAM) directly within the BIOS setup, delivering unparalleled performance for 4K video editing, and AI model training without the need for a discrete graphics card.
- 【Lastest GPU 8060S & XDNA 2 NPU】Built on the RDNA 3.5 architecture, the AMD Radeon 8060S Graphics iGPU features 40 compute units (2,560 stream processors). It delivers performance on par with NVIDIA's mobile RTX 4070, efficient encoding/decoding for AVC, HEVC, VP9, and AV1 video codecs. And It can connect 4 screens via HDMI & DisplayPort & Full Featured USB4 x2 to efficiently handle your tasks and meet your specific needs. Supports 8K/4K resolution displays.
- 【Dual LAN (2.5GbE+10GbE)& WiFi 7】The computer has double LAN, one is 2.5GbE (I226), the other is 10GbE(AQC113). provides more applications, such as firewall, soft routing, multichannel aggregation. Built-in WiFi module, support WiFi 7 and Bluetooth5.4. Known as 802.11be, Wi-Fi 7 promises up to 46Gbps theoretical throughput, making it 4.8x faster than Wi-Fi 6. and computer has 4 built-in NVMe SSD slots, 1 SD card slot, allowing you to expand its storage capacity.
- 【Engineered to Endure】The computer measures 7.13 x 7.24 x 2.99 inches. AI mini pc is encased in a premium all-aluminium chassis. Dual turbo CPU fans deliver silent, ultra-efficient cooling, To enable the computer to maintain stable operation for a long time. We offer up to 2 years warranty and lifetime professional customer service. Please feel free to contact us if any issues happened. thanks
When VS Code shows less context than expected
The context shown by an editor or model picker is not necessarily the amount Ollama allocates at runtime. Ollama’s integration documentation recommends configuring local context length to at least 64k for its documented workflow, then reloading VS Code and resending the prompt. This is integration guidance, not a universal requirement for every model, nor a hardware-sizing promise.
- Open Ollama’s settings and set the local context length to at least 64k, following the integration page’s guidance.
- Reload VS Code so the updated setting is picked up.
- Resend the prompt and check whether the desired context is available.
If the setting is unavailable or the result remains limited, consult the integration documentation for the current instructions rather than assuming the displayed context value reflects runtime allocation. The 64k recommendation is documented in Ollama’s VS Code integration guide.
Best Value
- 【AI Max+ 395 AI Workstation】16 cores, 32 threads, up to 5.1 GHz boost and 80 MB cache. Integrated Radeon 8060S graphics with 40 CUs, RDNA 3.5, delivers performance close to RTX 4060/4070 laptop GPUs. Triple-engine design(CPU+GPU+XDNA 2 NPU) with up to 126 TOPS total, including 50+ TOPS dedicated NPU for local AI inference and machine learning acceleration. Ideal for AI development, content creation, virtualization, data analysis, and demanding multitasking. Compact, high-performance workstation.
- 【256-bit LPDDR5X MAX 128GB】The LPDDR5X onboard memory reaches 8400 MT/s - 1.5x faster than DDR5 SODIMM. Unlock the full potential of your graphics with massive 128GB memory pooling. This system allows you to manually assign up to 128GB of the onboard RAM to serve as video memory (VRAM) directly within the BIOS setup, delivering unparalleled performance for 4K video editing, and AI model training without the need for a discrete graphics card.
- 【Lastest GPU 8060S & XDNA 2 NPU】Built on the RDNA 3.5 architecture, the AMD Radeon 8060S Graphics iGPU features 40 compute units (2,560 stream processors). It delivers performance on par with NVIDIA's mobile RTX 4070, efficient encoding/decoding for AVC, HEVC, VP9, and AV1 video codecs. And It can connect 4 screens via HDMI & DisplayPort & Full Featured USB4 x2 to efficiently handle your tasks and meet your specific needs. Supports 8K/4K resolution displays.
- 【Dual LAN (2.5GbE+10GbE)& WiFi 7】The computer has double LAN, one is 2.5GbE (I226), the other is 10GbE(AQC113). provides more applications, such as firewall, soft routing, multichannel aggregation. Built-in WiFi module, support WiFi 7 and Bluetooth5.4. Known as 802.11be, Wi-Fi 7 promises up to 46Gbps theoretical throughput, making it 4.8x faster than Wi-Fi 6. and computer has 4 built-in NVMe SSD slots, 1 SD card slot, allowing you to expand its storage capacity.
- 【Engineered to Endure】The computer measures 7.13 x 7.24 x 2.99 inches. AI mini pc is encased in a premium all-aluminium chassis. Dual turbo CPU fans deliver silent, ultra-efficient cooling, To enable the computer to maintain stable operation for a long time. We offer up to 2 years warranty and lifetime professional customer service. Please feel free to contact us if any issues happened. thanks
Keep the privacy boundary clear
- Use an Ollama local model for the code you want processed locally; do not switch to a cloud-hosted model for that request.
- Distinguish local model processing from VS Code features that depend on GitHub service connectivity.
- Check which model and provider are selected before sending sensitive code.
- Remember that the cited privacy statement is Ollama’s own policy, not an independent audit of the editor-plus-extension setup.
Ollama’s Privacy Policy says: “We do not collect, store, transmit, or have access to your prompts, responses, model interactions, or other content you process locally.” The statement concerns content processed locally by Ollama and should not be generalized to cloud models, unrelated extensions, or all activity on the workstation.
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