GPU Goldmines: Tunable WebGPU Kernels for Edge LLM Inference
As demand for LLM inference grows and consumer GPUs grow more capable, edge deployment is becoming increasingly attractive. WebGPU is compelling in this setting because it offers cross-platform portability and browser accessibility. However, these same strengths also make high performance difficult to achieve, as an implementation that performs well in one context may not perform well in another. To address this, we present GPU Goldmines, a framework for specifying, executing, and tuning WebGPU kernels. We instantiate GPU Goldmines with kernels for key LLM building blocks: matrix multiplication, matrix-vector multiplication, and flash attention. We characterize performance across diverse ...
| Authors | Rithik Sharma, Reese Levine, Zheyuan Chen et al. |
|---|---|
| Primary category | cs.DC |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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