Can spiking neurons make transformers efficient on any hardware?

Transformers hit two efficiency walls — training compute scales quadratically with sequence length, inference memory grows linearly — and building large models off NVIDIA hardware is its own challenge. SpikingBrain attacks both with three moves: linear and hybrid-linear attention with adaptive spiking neurons (event-driven sparse activation), a conversion-based training pipeline that starts from an existing open Transformer checkpoint (Qwen2.5-7B-base) rather than training from scratch, and system engineering tailored to a non-NVIDIA MetaX GPU cluster.

The keeper is the combination of cheapness and portability: the 7B linear model and 76B hybrid-linear MoE model match many open-source Transformers while using less than 2% of the training data, with linear/near-linear complexity that substantially accelerates long-sequence training. The conversion approach means the brain-inspired efficiency gains are reachable by adapting existing models, not retraining them — and the non-NVIDIA validation matters strategically for hardware diversification.

This extends the vault's efficiency-architecture thread. Since Can architecture choices improve inference efficiency without sacrificing accuracy? argue that architecture — not training-optimal scaling — governs inference cost, SpikingBrain is a concrete instance: it buys efficiency through attention linearity plus activation sparsity rather than parameter count, and its conversion pipeline rhymes with the broader move to obtain capability by adapting rather than retraining base models.