Disaggregation in Large Language Models: The Next Evolution in AI Infrastructure

"Large Language Model inference consists of two phases: prefill operations that achieve 90-95% GPU utilization with 200-400 operations per byte, and decode phases with 20-40% utilization and 60-80 operations per byte. Disaggregated serving architectures address the optimization inefficiency by separating prefill and decode operations onto specialized hardware clusters. Frameworks like vLLM, SGLang, and TensorRT-LLM have matured disaggregated serving with implementations demonstrating up to 6.4x throughput improvements and 20x reduction in latency variance."

"AI models are getting faster, but your infrastructure isn't. As large language models power everything from customer support to enterprise search, old-school, monolithic server setups are becoming a massive bottleneck and disaggregation might be the answer. Organizations implementing disaggregated architectures can reduce total infrastructure costs by 15-40% through optimized hardware allocation, improved energy efficiency, and elimination of over-provisioning high-end GPUs. Successful implementations require framework selection based on workload characteristics, migration planning with parallel deployment strategies, and addressing distributed architecture challenges."

LLM inference splits into a high-throughput prefill phase and a low-utilization, iterative decode phase with markedly different computational profiles. Prefill reaches 90–95% GPU utilization and demands roughly 200–400 operations per byte; decode operates at 20–40% utilization with about 60–80 operations per byte. Disaggregated serving separates prefill and decode onto specialized hardware clusters to match each phase to optimal resources. Mature frameworks such as vLLM, SGLang, and TensorRT-LLM enable substantial gains — up to 6.4× throughput and 20× lower latency variance. Organizations can cut infrastructure costs 15–40% by reallocating hardware, improving energy efficiency, and avoiding GPU overprovisioning. Successful deployment requires workload-aware framework selection, careful migration planning, and engineering for distributed systems.

Read at InfoQ