GenomeOcean Anywhere: Private WebGPU Inference for Genome MoEs
Genome foundation models are most useful where sequences are generated, yet the largest models need datacenter accelerators and a place to send private DNA. We ask whether a 15-billion-parameter genome mixture-of-experts (MoE) model can instead run on volunteers' web browsers, with the experts spread across many untrusted devices, without changing its predictions and without revealing the sequence to any single device. We build a system in which a trusted coordinator runs attention and routing while browser workers run every expert feed-forward network through hand-written WebGPU kernels, and we protect the expert inputs with real-valued Lagrange coded computing: each worker receives only...
| Authors | Guang Yang, Fengchen Liu |
|---|---|
| Primary category | cs.CR |
| Published | 27 September 2026 |
| Last updated at source | 1 October 2026 |
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