Large language models (LLMs) are accelerating in capability—but their infrastructure is falling behind. Despite massive advances in generative AI, current serving architectures are inefficient at inference time, especially when forced to handle highly asymmetric compute patterns. Disaggregated inference, the separation of input processing and output generation, offers a hardware-aware architecture that can dramatically improve performance, efficiency, and scalability.
Today, most state-of-the-art LLMs like GPT-4, Claude, and Llama rely on monolithic server configurations that struggle to serve diverse AI applications efficiently. This article explores the fundamental inefficiencies of conventional model serving, the technical reasoning behind disaggregation, and how it is reshaping inference performance at cloud scale.
Inference in large language models happens in two computationally distinct phases:
This split leads to radically different hardware requirements. Prefill benefits from high throughput compute (e.g., tensor core-heavy workloads), while decode suffers from irregular memory access patterns, poor batching efficiency, and low GPU utilization. In practical terms, the same GPU might run at 90% utilization during prefill, but only 25–30% during decode wasting energy and compute resources.
As IEEE Micro notes, phase-splitting LLM inference lets teams map prefill and decode to the right hardware class, improving throughput and cost.
Modern GPUs like the NVIDIA A100 and H100 are not designed to optimize both phases simultaneously. The H100's massive compute capabilities offer excellent prefill performance, but decode hits memory bottlenecks. Real-world metrics show decode operations achieving as little as 15–35% utilization of available hardware.
This asymmetry creates inefficiencies in cost, power consumption, and latency. Traditional co-located serving, where prefill and decode run on the same device, forces a lowest-common-denominator configuration, leading to overprovisioning of expensive accelerators for workloads that don’t need them.
Disaggregated serving architectures decouple prefill and decode phases across different hardware. This enables:
Instead of routing an entire request to a single GPU, the system splits the input and output phases to be served on the most appropriate compute resource, increasing efficiency and flexibility.
Anyscale, the company behind the Ray distributed computing framework, implemented continuous batching and disaggregated inference across prefill and decode pipelines. This resulted in:
By matching compute loads with real-time hardware capabilities, Anyscale created a serving system that is not only more efficient but also more resilient and scalable.
Several new inference frameworks have emerged to support disaggregated architectures:
These frameworks support complex LLM tasks, including large-context generation and multi-modal inference.
Implementing disaggregated inference requires changes across the stack:
Hardware innovation is starting to reflect these serving needs:
Vendors are now recognizing the disaggregation opportunity and building chips with inference-specific workloads in mind.
LLMs are becoming foundational infrastructure. Whether enabling conversational AI, enterprise automation, or real-time knowledge systems, they must serve users reliably and cost-effectively at scale.
Current inference strategies waste compute, raise operational costs, and limit scalability. Disaggregated serving is a necessary evolution, bringing software-hardware co-design principles to AI infrastructure at a time when they are most needed.
Anat Heilper is the Director of Software & Systems Architecture for AI and Advanced Technologies. With over 20 years of experience in machine learning systems and distributed infrastructure, she focuses on designing scalable AI deployment architectures. Connect with her on LinkedIn.
Disclaimer: The authors are completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.