Numbat: Building and Verifying a Self-Contained Machine-Learning Stack
Machine-learning systems are built almost exclusively on a few large Python-orchestrated frameworks, and they inherit those stacks' engineering costs: environments of hundreds of version-coupled packages, separate export toolchains for deployment, and the split between the language research is written in and the language products ship in. We report on the construction and verification of numbat, a machine-learning stack written in one general-purpose language (Zig) with no third-party runtime dependencies. The stack spans tensor computation, automatic differentiation, neural-network modules, mixed precision, multi-GPU training, data loading and monitoring; an SDK exposes it behind a stabl...
| Authors | Thang Tran, Lan Dang |
|---|---|
| Primary category | cs.SE |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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