SSMs vs. Transformers: The Battle for Long-Context Dominance

The rise of augmented reality and other latest applications demands AI models that can handle continuous and long-context inputs efficiently. Transformers, the reigning champion in AI architectures, stumble when faced with lengthy inputs due to their quadratic computational and memory overhead. This has opened the door for State Space Models (SSMs) and hybrid models that promise near-linear scaling.

SSMs Take the Lead

SSMs, once considered niche, are now stepping into the spotlight. Recent studies have shown that these models can efficiently handle millions of tokens without breaking a sweat. The reality is, while Transformers remain faster for short sequences (under 8K tokens) by a factor of up to 1.9x, SSMs turn the tables when contexts stretch to around 57K tokens. They're up to 4x faster in these scenarios, thanks to their linear computational complexity and a notable 64% reduction in memory usage.

What the Benchmarks Show

Here's what the benchmarks actually show: for on-device AI, particularly on consumer and embedded GPUs, SSMs outperform Transformers in long-context scenarios. This performance shift isn't just a mild preference but a dramatic inversion of capabilities. The architecture matters more than the parameter count in these cases, proving that newer isn't always better when scaled appropriately.

The Hardware Challenge

However, the game isn't over yet. SSMs face their own challenges. Custom SSM kernels, like selective scan, dominate inference runtime on edge platforms, accounting for over 55% of latency. Their sequential, element-wise operations, while hardware-aware, remain a bottleneck. So, is the SSM advantage enough to dethrone Transformers completely? That's the billion-token question.

Open-sourcing efforts, such as the SSM-Scope project, aim to bridge these gaps by providing a comprehensive benchmarking of these models specifically for long-context inference on consumer and embedded GPUs. As the field evolves, it's clear that both architectures have their place. But for now, if you're working with long-context data, the smart bet might just be on SSMs.