🤖 AI Summary
This study addresses the growing inefficiency of State-Space Models (SSMs), such as Mamba, on edge devices due to architectural optimizations increasingly tailored for high-throughput cloud platforms. Through a systematic analysis of the evolution from Mamba-1 to Mamba-3, we demonstrate a clear trend toward hyperscale GPU-centric design at the expense of edge-native efficiency and real-time performance. To reconcile this divergence, we introduce the concept of a “hyperscale lottery,” advocating for decoupling cloud-saturated strategies from core SSM architecture to restore edge feasibility. Empirical evaluations—including architectural comparisons, edge latency benchmarks, and parameter-scaling experiments—reveal that Mamba-3 incurs a 28% latency increase on edge hardware at 880 million parameters, rising to 48% for a 15-million-parameter variant, thereby substantiating significant degradation in edge efficiency.
📝 Abstract
The Hardware Lottery posits that research directions are dictated by available silicon compute platforms. We identify a derivative phenomenon, the Hyperscale Lottery, where model architectures are optimized for cloud throughput at the expense of algorithmic efficiency. While State-Space Models (SSMs) such as Mamba were lauded for their linear complexity, ideal for edge intelligence, their evolution from Mamba-1 to Mamba-3 reveals a systematic divergence from edge-native efficiency. We demonstrate that Mamba-3's architectural changes, designed to saturate hyperscale GPUs, impose a significant edge penalty: a 28% latency increase at 880M parameters, worsening to 48% for 15M-parameter models. We argue for decoupling cloud-scale saturation strategies from core architectural design to preserve the viability of single-user, real-time edge intelligence.