Disaggregating LLM Infrastructure: Solving the Hidden Bottleneck in AI Inference

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.

The Problem: LLM Inference Isn’t One Thing

Inference in large language models happens in two computationally distinct phases:

• Prefill: The model encodes the input prompt: a batch-parallel, compute-heavy task.
• Decode: The model generates tokens one at a time: a memory-bound, latency-sensitive task.

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.

Why Conventional Hardware Doesn’t Fit Both

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.

The Disaggregation Model: Split and Specialize

Disaggregated serving architectures decouple prefill and decode phases across different hardware. This enables:

• Up to 6× throughput improvement
• Better GPU utilization
• 15–40% cost savings

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.

Case Study: Anyscale’s Disaggregated LLM Serving

Anyscale, the company behind the Ray distributed computing framework, implemented continuous batching and disaggregated inference across prefill and decode pipelines. This resulted in:

• 23× throughput improvement
• Significant reduction in p50 latency
• Dynamic resource routing between specialized node types

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.

Engineering Frameworks for Disaggregated Inference

Several new inference frameworks have emerged to support disaggregated architectures:

• vLLM: Introduces PagedAttention and continuous batching for efficient memory use and dynamic request batching.
• SGLang: Features RadixAttention and structured generation with up to 6.4× improvement over baseline Llama-70B performance.
• DistServe (OSDI 2024): Demonstrated 4.5× goodput improvement and reduced latency variance through phase separation.

These frameworks support complex LLM tasks, including large-context generation and multi-modal inference.

System Design Considerations

Implementing disaggregated inference requires changes across the stack:

1. Scheduling & Routing Schedulers must understand phase-level load characteristics and dynamically route to the correct node type based on latency sensitivity and compute demand.
2. Network Architecture Low-latency interconnects are critical. Service mesh patterns and RPC optimization play an essential role in ensuring that prefill-decode phase handoffs remain efficient.
3. Monitoring & Auto-Scaling Observability tools must track not just node utilization but phase-specific efficiency. Auto-scaling policies need to adapt to varying prefill vs. decode ratios across workloads.

The Hardware Outlook

Hardware innovation is starting to reflect these serving needs:

• Chiplet-based designs allow for flexible resource pairing
• Near-memory compute reduces data movement for decode-heavy tasks
• Memory-compute co-design aims to better match the demands of token-by-token generation

Vendors are now recognizing the disaggregation opportunity and building chips with inference-specific workloads in mind.

Why It Matters

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.

About the Author

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.