Year-end reflection time. Been digging into Inference Labs lately, and their dsperse architecture caught my attention. Here's the thing—it's a clever approach to how large language models get structured. Instead of running everything through a monolithic pipeline, the system fragments model processing into distributed components. This kind of modular thinking matters for scaling. You get better resource allocation, lower latency, and the flexibility to upgrade individual layers without rebuilding the entire stack. Not groundbreaking on paper, but in practice? It's the kind of engineering detail that separates projects punching above their weight from those stuck in proof-of-concept limbo. Worth tracking if you're following how infrastructure teams are solving computational bottlenecks in 2025.