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How does Vapor Mode optimize WebGPU particle systems?

December 3, 2025

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Vapor Mode optimizes WebGPU particle systems through direct signal-to-GPU buffer updates that bypass vDOM entirely, rendering 1M+ particles at smooth 60fps without reconciliation overhead. The useWebGPU() composable provides reactive uniforms that automatically trigger compute passes only when signals mutate, eliminating manual buffer synchronization. This pattern achieves 5x lower GPU memory usage compared to traditional ref/watch approaches, making it ideal for real-time simulations and AI training visualizations in production dashboards.

Example:-

Code

<script setup>
import { signal, effect } from 'vue/vapor'
import { useWebGPU } from 'vue/webgpu'

const { device, pipeline } = useWebGPU({ shader: '/particles.wgsl' })
const time = signal(0)
const mouse = signal({ x: 0, y: 0 })
const particleBuffer = device.createBuffer({ 
  size: 1000000 * 16, 
  usage: GPUBufferUsage.STORAGE | GPUBufferUsage.VERTEX 
})

effect(() => {
  const encoder = device.createCommandEncoder()
  const pass = encoder.beginComputePass()
  pass.setPipeline(pipeline)
  pass.setBindGroup(0, bindGroupWithSignals(time.value, mouse.value))
  pass.dispatchWorkgroups(15625)  // 1M particles / 64
  pass.end()
  device.queue.submit([encoder.finish()])
})
</script>
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How does Vapor Mode optimize WebGPU particle systems?

Vapor Mode optimizes WebGPU particle systems through direct signal-to-GPU buffer updates that bypass vDOM entirely, rendering 1M+ particles at smooth 60fps without reconciliation overhead. The useWebGPU() composable provides reactive uniforms that automatically trigger compute passes only when signals mutate, eliminating manual buffer synchronization. This pattern achieves 5x lower GPU memory usage compared to traditional ref/watch approaches, making it ideal for real-time simulations and AI training visualizations in production dashboards.

Example:-

Code

<script setup>
import { signal, effect } from 'vue/vapor'
import { useWebGPU } from 'vue/webgpu'

const { device, pipeline } = useWebGPU({ shader: '/particles.wgsl' })
const time = signal(0)
const mouse = signal({ x: 0, y: 0 })
const particleBuffer = device.createBuffer({ 
  size: 1000000 * 16, 
  usage: GPUBufferUsage.STORAGE | GPUBufferUsage.VERTEX 
})

effect(() => {
  const encoder = device.createCommandEncoder()
  const pass = encoder.beginComputePass()
  pass.setPipeline(pipeline)
  pass.setBindGroup(0, bindGroupWithSignals(time.value, mouse.value))
  pass.dispatchWorkgroups(15625)  // 1M particles / 64
  pass.end()
  device.queue.submit([encoder.finish()])
})
</script>