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Yes—SysVis.AI documents a browser-local mode for generating and analyzing architecture diagrams using WebGPU. Its listed local models are Qwen3-0.6B for text prompts and ViT-GPT2 for image analysis. “100% locally” describes this mode, not every configuration: the project also lists cloud AI integrations, so check the selected provider and current privacy behavior before entering sensitive material.
What SysVis.AI does
SysVis.AI is an AI-assisted system-design diagram editor. Its publisher says it can take natural-language prompts, images, or Mermaid code and produce editable diagrams. Listed export formats are PNG, JPG, SVG, JSON, Mermaid, and React. These are documented capabilities, not an independent assessment of diagram quality.
The publisher describes a stack that includes React Flow (XY Flow) for diagramming, Monaco Editor, Zustand, WebLLM for WebGPU inference, and Transformers.js. It also lists Google Gemini, OpenAI, and Ollama integrations alongside the browser-local models. The exact features and provider behavior may depend on the current release and configuration.
What “100% locally” means—and what it does not
In the documented local mode, inference runs in the browser using downloaded models and WebGPU rather than requiring a hosted AI provider for those model operations. That does not establish that every SysVis.AI configuration is local: cloud-provider options are also listed. Nor does it establish that every part of an app’s operation, such as hosting or other network requests, happens only on your device.
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The documentation does not fully specify current data flows for every release or integration. Before using confidential diagrams, confirm which model/provider is selected and review the current product documentation. Avoid putting secrets, credentials, or sensitive architecture details into a configuration whose data handling you have not verified.
Browser and device requirements
Local AI requires a browser and device with working WebGPU support. SysVis.AI’s publisher names Chrome 113+ and Edge as compatible browser options, but a browser name or version alone does not guarantee WebGPU works on every operating system, device, or GPU. Check current browser and device implementation status at WebGPU.org. The site describes WebGPU and WGSL as W3C standards for GPU acceleration on the web.
If WebGPU is unavailable or disabled, the browser-local models may not run as documented. The project’s cloud integrations are a separate configuration, not a guarantee that a particular fallback is available or selected automatically.
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Local inference requires downloading the models. The SysVis.AI publisher lists the following approximate sizes and runtimes on its Docker Hub overview, accessed October 7, 2026. The page gives no benchmark methodology or publication year for these figures, so treat runtimes as unverified publisher estimates rather than performance guarantees.
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| Local task | Listed model | Approximate model size | Publisher-reported time |
|---|---|---|---|
| Text generation from a prompt | Qwen3-0.6B | About 500 MB | About 30–60 seconds |
| Image analysis | ViT-GPT2 | About 300 MB | About 8–10 seconds |
Actual download and processing times can vary by connection, device, browser, and workload. Plan for the initial model downloads before trying the local workflow, and do not assume the publisher’s estimates will match your device.
How to run it with Docker
The publisher’s Docker Hub overview gives this example command:
docker run -d -p 8338:80 vndangkhoa/sys-arc-visl:latest- Install and start Docker on your machine.
- Run the command in a terminal. It downloads the image if needed, starts a container in the background, and maps port 8338 on your machine to port 80 in the container.
- Open http://localhost:8338 in a browser.
- Choose the browser-local AI mode and confirm WebGPU works in that browser and device before relying on local inference.
The image tag and instructions can change. Check the current Docker Hub image overview for the latest tag details before deploying. The page identifies the project as SysVis.AI/KV-Graph; verify that the image and features you intend to use correspond to the current release.
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The same publisher overview describes a development setup using Node.js 18 or later and npm or pnpm. It instructs developers to clone the kv-graph repository, install dependencies, and run the development server. Consult the project’s current repository instructions for the exact clone URL and commands; they are not specified here, and package scripts or prerequisites may change.
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What to verify in generated diagrams
Treat an AI-generated diagram as a draft, not as proof of how a real system works. The product documentation lists diagram creation, image analysis, and exports, but does not provide independent quality testing or establish that outputs are validated against a codebase or live system behavior.
- Check that services, databases, queues, and external dependencies match your actual architecture.
- Verify arrow direction, protocols, trust boundaries, and data flows with the system’s owners or source documentation.
- Review image-derived diagrams especially closely; visual analysis can omit or misinterpret details.
- Keep the editable source or export alongside the diagram so reviewers can correct it and track changes.
This distinction matters across browser-based diagram tools. For example, FreeToolOnline describes analysis based on project marker files and explicitly distinguishes that from tracing actual code connections; that kind of detection should not be mistaken for a verified architecture audit. See its Architecture Diagram Maker explanation.
When this setup makes sense
SysVis.AI’s local mode may suit someone who wants to experiment with prompt- or image-based diagram drafts in a browser and has a WebGPU-capable environment. It is less suitable as a sole source of truth for a production architecture, or as a privacy guarantee without checking the selected provider and current data handling. The practical decision is not simply local versus cloud: consider the input method, editability and export needs, device compatibility, model downloads, deployment approach, and whether a human will validate the result.
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