Meganeura: Portable GPU Training and Inference through Vulkan and Metal
Training and deployed inference often cross export, conversion, and platform-specific runtime boundaries. Meganeura asks whether one compact native compiler can span both phases on consumer GPUs. Its typed static graph, automatic differentiation, optimizer, checkpoint, memory planner, and runtime lower specialized programs through Vulkan and Metal. We compare five matched workloads with PyTorch on NVIDIA and AMD discrete GPUs, an AMD APU, Apple silicon, and an Intel iGPU. The protocol separates strict f32 from validated fast paths and gates forward and backward independently. Forty-eight of 50 device-workload-mode cells pass both gates; the other two share one unresolved backward-referenc...
| Authors | Dzmitry Malyshau |
|---|---|
| Primary category | cs.LG |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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