Abstract

A synthesis paper: train and serve collapse into a single Rust engine with one tensor type, one quantization core, and one device backend — eliminating the format-break tax of training in PyTorch, exporting to GGUF/ONNX, and serving on a third engine. Formalizes a unified bandwidth-budget theorem and QLoRA bit-exactness, with every claim sourced to a measured ROCm campaign on consumer hardware. Scrupulously scoped: claims no speed win over NVIDIA and no zero-leakage guarantee.