Google Colab offers a GPU notebook, but GPU models, idle timeouts and resource availability vary. Free notebooks can run for at most 12 hours, depending on availability and usage. You prepare Blender and file transfers in the notebook. JANCTION Render is a render service: send the .blend or bpy script by MCP, CLI or HTTP, get a 4-frame preview in about 4 seconds and the final MP4 from an NVIDIA RTX PRO 6000 datacenter GPU. Each new key starts with a welcome credit of up to 500 JPY (14 days); after that, previews stay free up to 2 GPU-minutes a day and finals cost 0.1 JPY per GPU-second. Use Colab when you need a general GPU notebook; use JANCTION Render when the job is to render Blender, especially from an AI agent. AI agents connect at https://render.janction.jp/mcp (remote MCP with OAuth; connecting creates a free key).
Updated 2026-11-12
What it does Your own .blend, bpy script or 3D file (glTF/GLB, FBX, USD, OBJ...) rendered to images or video on a cloud NVIDIA GPU, from an AI app, the terminal or the browser. The first preview needs no payment.
What it does not do It does not speed up the Blender viewport on your machine, does not take non-Blender video, simulations or general GPU jobs, and an AI that is not connected cannot render on its own.
Try your own file for freeBuild it with an AI and try
Browser only; no connector or CLI needed. A 100 JPY welcome credit for a new key (14 days, no card; one per network every 30 days, further keys on the same network share what is left), and 400 JPY more for saving a card that is not charged (once per card); after that, previews stay free up to 2 GPU-minutes a day and finals cost 0.1 JPY per GPU-second.
| Google Colab (free tier) | JANCTION Render | |
|---|---|---|
| set-up | open a notebook, download and unpack Blender, mount Drive or upload files, every session | none: add the MCP connector, or pip install janction-render, or curl |
| GPU | variable; a particular GPU such as T4 is not guaranteed | NVIDIA RTX PRO 6000 Blackwell (96 GB) |
| limits | up to 12 hours on the free tier, variable idle timeout and usage limits; GPU access is not guaranteed | welcome credit of up to 500 JPY per new key (14 days), then previews free up to 2 GPU-minutes a day; up to 3,000 frames per instruction (automatically split internally), 1080p on free time and 4K on prepaid credit; jobs queue when every GPU is busy |
| AI agent access | AI can assist notebook work; free managed runtimes restrict remote control and bypassing the notebook UI | MCP tools (preview, estimate, final, status, download) and an HTTP API |
| preview loop | whatever you script | 4-frame preview in about 4 GPU seconds, estimate before the final |
| files | Drive mount or manual upload; results stay in the session or Drive | upload by API; results as links for 24 hours, files deleted 24 hours after last use (7 days, normally, for keys that have paid 500 JPY or more in total) |
| cost | free tier, then Colab Pro plans | welcome credit of up to 500 JPY per new key, then 0.1 JPY per GPU-second (previews free up to 2 GPU-minutes a day) |
| custom add-ons | yes, you control the install | no: Blender 3.6 to 5.2 with Cycles, HDRI presets, standard importers |
Source checked 2026-10-02: Google Colab official FAQ (resource limits and restricted activities). Hardware and limits vary; no fixed 90-minute idle timeout is promised. JANCTION figures are our measurements.
| job | GPU time | note |
|---|---|---|
| preview, 4 frames (720p split in 4 tiles, 16 samples, denoised) | about 4 s | round trip about 8 s; the image comes back inline |
| final, 24 frames at 1280x720, 64 samples → MP4 | about 60 s | measured 2026-09-30 / 10-01 (59 s and 58 s) |
| final, one 1920x1080 frame at 128 samples | about 11 s | the estimator's baseline; heavy scenes take longer |
These are individual observations of simple scenes (2026-09-30 to 10-01 UTC, NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition, 96 GB). The 4-GPU-second example used a rotating cube, four 640x360 tiles and 16 samples. GPU time includes Blender startup and scene loading; it is distinct from completion time including queue and transfers. See conditions, round trips and reproduction files. These are not speed guarantees.
render_estimate gives the number before rendering; once a scene has been rendered once, the estimate uses that scene's own measurement.
Measured cases with resolution, samples, frames and GPU seconds: benchmarks.
If you want the notebook route: download the Blender Linux build, unpack it, then run it headless on the GPU:
!./blender-5.0.1-linux-x64/blender -b scene.blend -E CYCLES -o //out_ -f 1 -- --cycles-device CUDA
Keep the scene and output on Drive, because the session's disk disappears when it ends. Re-run the install each new session.
| from | how |
|---|---|
| A file on this computer (.blend, 3D file) | Drop it at /upload to get a 12-hour link (no sign-up), then tell the AI "Render this file: <link>". From inside Blender use the add-on; from a terminal the CLI uploads directly |
| Claude.ai (web / desktop / mobile) | Plugins (older menu: Customize → Connectors) → Add custom connector → https://render.janction.jp/mcp. "Connect" opens a page that creates a free key |
| Claude Team / Enterprise | An owner adds https://render.janction.jp/mcp under Organization settings → Connectors; each member then presses Connect with their own account. In organizations that turn Skills off, Claude Desktop also blocks plugins from November 30 (use the connector URL, or ask the admin to turn Skills on) |
| ChatGPT | Plugins (left sidebar) → Add → Create plugin → https://render.janction.jp/mcp (OAuth). Attach your .blend to the chat. On Business workspaces an admin adds it and shares it |
| Claude Code | claude mcp add --transport http janction-render https://render.janction.jp/mcp, then /mcp to authenticate |
| Claude Code on the web (claude.ai/code) | The same connector as Claude.ai: Plugins (older menu: Customize → Connectors) → Add custom connector → https://render.janction.jp/mcp. Works without changing the session's network settings (guide) |
| Codex | codex mcp add janction-render --url https://render.janction.jp/mcp (the sign-in page opens right away; if Codex later says the server needs login, run codex mcp login janction-render; with a key, add --bearer-token-env-var JANCTION_RENDER_API_KEY) |
| Cursor / VS Code | Add to Cursor Add to VS Code Add to LM Studio The button opens the editor; confirm and it is added. Manual: Streamable HTTP at https://render.janction.jp/mcp |
| Antigravity CLI / Gemini CLI | agy plugin install https://github.com/JasmyLab-JANCTION/janction-render or gemini extensions install https://github.com/JasmyLab-JANCTION/janction-render (the MCP server and the workflow skill) |
| Kimi Code (Kimi K3 and other models) | /plugins install https://github.com/JasmyLab-JANCTION/janction-render (the MCP server and the workflow skill), or add https://render.janction.jp/mcp as a url entry under mcpServers in ~/.kimi-code/mcp.json and run /mcp-config login janction-render (OAuth). Guide |
| OpenCode | Add https://render.janction.jp/mcp under mcp in ~/.config/opencode/opencode.json with "type": "remote", then opencode mcp auth janction-render (OAuth). Guide |
| Qwen Code | qwen extensions install https://github.com/JasmyLab-JANCTION/janction-render (the MCP server and the workflow skill), or qwen mcp add --transport http janction-render with https://render.janction.jp/mcp (stored as httpUrl). Guide |
| GitHub Copilot CLI | copilot mcp add --transport http janction-render with https://render.janction.jp/mcp, then /mcp auth janction-render in copilot (OAuth). With the skill: copilot plugin marketplace add JasmyLab-JANCTION/janction-render, then copilot plugin install janction-render@janction-render. Guide |
| DeepSeek Harness (dsh) | One @deepseek-ai/dsh-mcp-client entry: transport: streamable-http, url: https://render.janction.jp/mcp, headers Authorization: Bearer with a key from POST /v1/keys (no OAuth). DeepSeek guide (API function calling too) |
| Grok / Perplexity / Le Chat | Add a custom (remote MCP) connector with https://render.janction.jp/mcp in each app's Connectors settings (Grok: Plugins → New connector → Custom, guide; Perplexity: Settings → Connectors → Custom → Remote, Pro / Max / Enterprise, OAuth; Le Chat: workspace admin, OAuth or a Bearer key) |
| Gemini app (gemini.google.com) | On the web: Settings → Connected Apps → Custom apps, add the MCP URL https://render.janction.jp/mcp (web only, English interface, with activity saving on). Sign-in is the OAuth consent page |
| Manus | Connectors → Create → Add MCP by URL with https://render.janction.jp/mcp (remote MCP only; OAuth or a Bearer key) |
| MiniMax (MiniMax Code) | One streamable-http entry in the MCP settings: url https://render.janction.jp/mcp, and Authorization: Bearer jr_… in headers |
| Notion (Notion Agent) | Business / Enterprise: Settings → Connections → Discover → Add Custom MCP → https://render.janction.jp/mcp, then sign in (the button shows when a workspace admin allows custom MCP servers) |
| JetBrains (AI Assistant, Junie) | AI Assistant: Settings → Tools → AI Assistant → Model Context Protocol (MCP) → Add → connect over HTTP with {"mcpServers": {"janction-render": {"url": "https://render.janction.jp/mcp"}}}. Junie: the same url in ~/.junie/mcp/mcp.json (Authorize opens the browser), or "headers": {"Authorization": "Bearer jr_…"} |
| Kiro (IDE, CLI) / Amazon Q Developer CLI | Kiro: "url": "https://render.janction.jp/mcp" under mcpServers in ~/.kiro/settings/mcp.json (in the IDE, open a folder: MCP SERVERS appears in the Kiro panel and the authorization page opens; in the CLI, /mcp auth janction-render). With a key: "headers": {"Authorization": "Bearer ${JANCTION_RENDER_API_KEY}"} (add the variable to Mcp Approved Env Vars). Amazon Q Developer CLI: "type": "http" and the same url in the agent configuration, then /mcp to sign in |
| Microsoft Copilot Studio | Tools → Add a tool → New tool → Model Context Protocol → Server URL https://render.janction.jp/mcp. Authentication: OAuth 2.0 with Dynamic discovery, or API key (Header, name X-API-Key, value jr_…). Set the agent's Settings → Generative AI → Orchestration to Yes (generative orchestration), or it will not use the tool |
| LM Studio (0.3.17 or later) | Program tab → Install → Edit mcp.json: {"mcpServers": {"janction-render": {"url": "https://render.janction.jp/mcp", "headers": {"Authorization": "Bearer jr_…"}}}} |
| Cherry Studio | Settings → MCP Servers → Add → Import from JSON: {"mcpServers": {"janction-render": {"type": "streamableHttp", "baseUrl": "https://render.janction.jp/mcp"}}}. Turn it on and press "Sign-in required." to open the consent page in the default browser. If no tools show after signing in, turn the server off and on again (with a key: "headers": {"Authorization": "Bearer jr_…"}) |
| Windsurf, Cline, Goose, Amp, Auggie, Warp, Zed, Roo Code, Continue and other MCP clients | Streamable HTTP at https://render.janction.jp/mcp; OAuth or Authorization: Bearer <api key> |
| Claude Desktop and other stdio-only clients (npm) | npx -y janction-render (in the client's mcpServers: {"command": "npx", "args": ["-y", "janction-render"]}). The npm package janction-render bridges stdio to https://render.janction.jp/mcp with OAuth; --key jr_… for a key you have, --local for the Python stdio server through uv |
| Terminal (stdio, sends local files) | claude mcp add janction-render -e JANCTION_RENDER_SERVER=https://render.janction.jp -- uvx --from janction-render janction-render-mcp |
| Inside Blender (for people) | The add-on; extension repository https://render.janction.jp/extensions/index.json. Maya / Houdini / Cinema 4D: submit tools |
Using JANCTION Render, build a glossy red cube rotating on a gray floor and show me a 4-frame preview. If it looks right, render 24 frames at 1280x720 as an MP4.
To render a .blend or 3D file that is on your computer, drop it at /upload to get a 12-hour link (no sign-up) and tell the AI "Render this file: <link>". To test the connection first: "Render a preview of https://render.janction.jp/samples/cube_scene.py".
It depends on the assigned GPU, scene and settings. We have not published a controlled Colab comparison, so we cannot claim a speed advantage. JANCTION-only measurements are on the benchmarks page.
AI can assist notebook work. Free managed runtimes restrict remote control and bypassing the notebook UI; check the official FAQ before automating a workflow. JANCTION Render provides dedicated render tools through MCP and HTTP.
Yes. Save the .blend, and the CLI or the agent uploads it with the textures it references.
Inputs and results are deleted 24 hours after last use (7 days, normally, for keys that have paid 500 JPY or more in total); nothing is used for training.
