ChartGPU – WebGPU-powered charting library (1M points at 60fps)
Creator here. I built ChartGPU because I kept hitting the same wall: charting libraries that claim to be "fast" but choke past 100K data points. The core insight: Canvas2D is fundamentally CPU-bound. Even WebGL chart libraries still do most computation on the CPU. So I moved everything to the GPU via WebGPU: - LTTB downsampling runs as a compute shader - Hit-testing for tooltips/hover is GPU-accelerated - Rendering uses instanced draws (one draw call per series) The result: 1M points at 60fps with smooth zoom/pan. Live demo:…
In plain words
ChartGPU is a WebGPU-powered charting library designed for developers who need to visualize large datasets. It renders one million data points at 60 frames per second by offloading computation to the GPU—including downsampling, hit-testing, and rendering—rather than relying on CPU-bound approaches. The library supports line, area, bar, scatter, pie, and candlestick charts with smooth zooming and panning. It is MIT licensed and available via npm.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Creator here. I built ChartGPU because I kept hitting the same wall: charting libraries that claim to be "fast" but choke past 100K data points. The core insight: Canvas2D is fundamentally CPU-bound. Even WebGL chart libraries still do most computation on the CPU. So I moved everything to the GPU via WebGPU: - LTTB downsampling runs as a compute shader - Hit-testing for tooltips/hover is GPU-accelerated - Rendering uses instanced draws (one draw call per series) The result: 1M points at 60fps with smooth zoom/pan. Live demo: https://chartgpu.github.io/ChartGPU/examples/million-points/ Currently supports line, area, bar, scatter, pie, and candlestick charts. MIT licensed, available on npm: `npm install chartgpu` Happy to answer questions about WebGPU internals or architecture decisions.
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