High-throughput inference remains a central challenge for equivariant machine-learned interatomic potentials. Here we introduce FACTO, an atom-centered equivariant architecture that confines evolving learned state and dense channel mixing to atoms, expressing neighbor interactions as fully fusible reductions. We complement this design with custom register-accumulated aggregation kernels and tensor-core FP32 emulation, accelerating inference while retaining FP32-level energy conservation under emulated arithmetic in molecular dynamics. Comprehensive evaluations on Matbench Discovery, MLIP Arena and MatCalc demonstrate high throughput alongside strong predictive accuracy and physical reliability. On Matbench Discovery, the four FACTO-OAM variants show top-tier performance by overall rank sum among 46 models with complete scores, advancing the accuracy-throughput Pareto frontier. On MLIP Arena, FACTO achieves state- of-the-art stability under extreme heating and compression, alongside high fidelity in hydrogen combustion. On MatCalc, FACTO outperforms the compared baselines in structural and elastic predictions while retaining competitive accuracy for energetic and thermal properties.