Vercel GPU (vgpu)
NexusAi Summary: Vercel GPU (vgpu) is a WebGPU library purpose-built for agents and shader workflows. It unifies browser and headless rendering, streamlines WGSL packaging, and ships a CLI for docs, validation, and diagnostics—enabling interactive canvases, image/video exports, and CI-safe renders from one shader.
NexusAi Overview
Initialize the GPU, create a surface for your canvas, bind a compiled effect, set uniforms, then stream frames—exactly the same logic that runs headless for tests and media outputs. Wrap existing GPU buffers without copies, feed results into shaders, and compare snapshots in CI.
What It Does
Ideal for agent developers orchestrating WebGPU tools, graphics engineers shipping real-time effects, and ML practitioners needing zero-copy visualization of GPU compute outputs. Front-end teams can ship interactive canvases in frameworks like Next.js, while QA and platform engineers gate changes with headless renders and snapshot comparisons in CI. The result is a unified path from experimentation to dependable deployments without hand-rolled pipelines.
- Import WGSL modules like TypeScript and emit compact, tree-shaken shader builds.
- Run the same effect in browsers, Node.js headless, images, and videos.
- Reflect GPU bindings automatically and validate layouts before work hits the device.
- Automate CI snapshots that compile, render, and compare outputs for graphic regressions.
- Use the CLI for docs, examples, schema checks, and runtime diagnostics.
Why It Stands Out
Who It's For
Install with pnpm add vgpu or run npx vgpu to explore docs and examples. Add the skill via npx skills add vercel-labs/vgpu and register the MCP endpoint with npx -y add-mcp ... -g. In code, init() the device, create a surface(canvas), compile an effect from WGSL, set uniforms, and drive frames. Use the CLI to validate WGSL, inspect bindings, and run doctor checks. For CI, script a headless render and snapshot comparison to guard against regressions.
NexusAi: Write a shader once, deploy it across canvas, headless, and CI.
Getting Started
vgpu packages a pragmatic WebGPU toolchain for agents and production apps: one shader, multiple targets, reflective WGSL builds, and a CI story that actually holds the line. With examples, diagnostics, and a skill-driven CLI, teams move from prototype to dependable pipelines faster—without custom glue or brittle scripts.
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Vercel GPU (vgpu)
Vercel Labs explores the future of web and AI tooling at Vercel. The team builds frameworks, SDKs, and agent infrastructure that favor developer ergonomics, performance, and reliability—spanning the Agent Stack, skills, MCP integrations, and production workflows that connect design, code, and deployment.
Vercel GPU (vgpu) is a developer library that turns WebGPU into a practical, agent-friendly runtime for shaders and GPU compute. Write a WGSL effect once and run it interactively in the browser or headless in Node.js, then emit images, video, or snapshots for testing. vgpu resolves module graphs, reflects bindings, prunes unused declarations, and emits compact shader source at build time, so teams ship smaller, predictable pipelines. A guided CLI exposes docs, examples, validation, and runtime diagnostics, helping catch layout and portability issues early. Example projects span interactive graphics, depth estimation with ONNX Runtime Web, and a Next.js logo shader—illustrating both rendering and compute-oriented patterns. For CI, vgpu compiles, renders a frame, and compares snapshots to guard against regressions. Together, these pieces make WebGPU approachable for agents, production apps, and continuous testing without bespoke glue code.
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