State Space Models as CPU-Native Neural Network Architectures

We present experimental evidence that State Space Models (SSMs) are structurally advantageous for neural network inference on CPU and ARM architectures. By porting BitMamba-2, a 1.58-bit quantized Mamba implementation, from x86 AVX2 to ARM NEON, we achieve 82.5 tokens/sec (255M parameters) and 29.6 tokens/sec (1B parameters) on an Apple M1 processor — the first published ARM benchmark for this model family. We experimentally validate the O(1) memory property of SSMs: generation speed remains perfectly constant across sequence lengths from 50 to 200+ tokens, in contrast to Transformer-based models whose memory grows linearly with context via the KV cache. At comparable model weight sizes (~600 MB), the SSM achieves throughput competitive with quantized Transformers (~30–40 tokens/sec) while offering constant memory footprint and 1.58-bit compression (vs. 4-bit for Transformers). These results support the thesis that mathematical reformulations — here, the combination of state space recurrence with ternary quantization — can make non-GPU inference structurally competitive rather than merely tolerable.