Engineering @ Scale — 2026-05-03#

Signal of the Day#

Cloudflare is tackling the exorbitant cost and performance bottlenecks of global LLM inference by architecturally decoupling the input processing phase from the output generation phase. This allows them to route heavily asymmetric workloads to purpose-optimized hardware systems rather than relying on monolithic, generalized compute environments.

Deep Dives#

Decoupling Input and Output for Global LLM Inference · Cloudflare · Cloudflare Builds High-Performance Infrastructure for Running LLMs To serve large language models across its distributed global network, Cloudflare faced strict architectural constraints around the high cost of AI hardware and the massive volumes of incoming and outgoing text. Their engineering team addressed this by physically separating the inference workload, decoupling the model’s input processing phase from its output generation phase. Rather than relying on standard monolithic instances to handle the entire request lifecycle, they routed these distinct workloads to different systems optimized specifically for their unique compute and memory bandwidth profiles. The key tradeoff in this architecture is accepting the added network overhead and complex state transfer required between the separate input and output systems in order to achieve significantly higher overall hardware utilization. This split-inference approach serves as a highly instructive blueprint for other engineering teams struggling to scale asymmetric, multi-phase compute workloads on expensive, heavily constrained infrastructure.