LLM Serving Fairness: No more noisy neighbors

The humble apartment wall is the original fairness algorithm. It separates your quiet evening from your neighbor's drum solo, and its failure mode is the "noisy neighbor" problem. Cohere's latest work on LLM serving fairness tackles exactly this—but the walls being built are made of scheduling algorithms, and the neighbors are large language models sharing a GPU. When one user fires off a massive batch of requests, it's like throwing a rave in the server rack; everyone else's tokens suddenly arrive late. The fix is a mechanism that throttles the greedy and protects the quiet—a digital noise ordinance for the age of artificial minds. Here's where it gets weird and wild: we're watching the birth of AI civics. The problem of fair resource allocation is as old as human society—from the Roman grain dole to net neutrality—but this time, the citizens are disembodied neural networks and their human proxies. Cohere's approach echoes John Rawls' veil of ignorance: design the rules without knowing whether you'll be the heavy user or the light one, and you'll build something fair. The algorithm becomes a social contract etched into silicon, a Rawlsian bargain executed in