Key Takeaways

• 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.
• 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.

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.

Introduction to Large Language Models

Large Language Models have transformed from research projects to critical business infrastructure, powering everything from customer service chatbots to content creation platforms. Models like GPT-4, Claude, and Llama operate with billions of parameters, requiring sophisticated computational infrastructure to serve predictions efficiently.

The fundamental challenge lies in LLM inference’s dual nature as shown in Figure 1: an initial “prefill” phase that processes input context simultaneously, followed by iterative “decode” phases that generate output tokens one by one. These phases have completely different computational characteristics, creating optimization challenges that traditional serving architectures cannot efficiently address.

Figure 1: Prefill and Decode phases characterization

Understanding Prefill and Decode Phases

The prefill phase exhibits high computational intensity with 200-400 operations per byte of memory access, achieving 90-95% GPU utilization on modern accelerators. Multiple requests can be batched together efficiently, making it ideal for compute-intensive hardware.

In contrast, the decode phase operates with only 60-80 operations per byte, achieving just 20-40% GPU utilization due to memory bandwidth constraints. Each token generation requires accessing large key-value caches with unpredictable patterns, making efficient batching difficult.

This creates a 5-10x difference in computational characteristics between phases. Different applications amplify this challenge: summarization tasks are prefill-heavy (80-90% of compute time) with large inputs and concise outputs, while interactive chatbots require sub-200ms response times with variable input lengths. Agentic AI systems manage complex 8K-32K+ token contexts with memory-intensive tool integration, requiring entirely different optimization strategies.

Why Single Accelerators Can’t Optimize Both Phases

Modern AI accelerators like NVIDIA’s H100 and A100 GPUs are designed for specific computational patterns that align well with either prefill or decode, but not both simultaneously. The H100’s 3.35 TB/s memory bandwidth and 3x compute improvement over A100 makes it excellent for prefill’s compute-intensive operations. However, the A100 actually achieves higher efficiency during decode phases due to its different memory architecture.

This optimization dilemma stems from fundamental hardware trade-offs. Prefill phases benefit from high compute density and large on-chip memory, while decode phases require high memory bandwidth and low-latency access patterns. Memory hierarchy optimizations that help one phase often hurt the other, and power efficiency varies dramatically based on utilization patterns.

During LLM inference, the prefill phase utilizes tensor cores efficiently, often approaching maximum hardware capacity. In contrast, the decode phase generally achieves much lower utilization, with values commonly reported as one-third or less compared to prefill. Energy efficiency is also significantly higher during the prefill phase; studies have shown 3–4 times better efficiency per operation relative to the decode phase. These differences in hardware performance and energy consumption emphasize the limitations of monolithic inference.

The Rise of Disaggregation in LLM Inference Serving

Contrary to common assumptions, vLLM was among the first frameworks to implement purpose-built disaggregated LLM serving when it launched in June 2023. While DeepSpeed implemented heterogeneous inference capabilities in 2022, its focus was primarily on model parallelism rather than prefill-decode disaggregation for serving optimization.

vLLM’s breakthrough came with PagedAttention for efficient key-value cache management and continuous batching for improved throughput. As shown in figure 2, Version 0.6.0 demonstrated 2.7x throughput improvements for Llama 8B models and 5x faster time-per-output-token, setting the stage for broader industry adoption.

SGLang followed with RadixAttention and structured generation capabilities, achieving 6.4x throughput improvements over baseline implementations and consistently outperforming competitors by 3.1x on Llama-70B models. The academic community solidified the theoretical foundation with DistServe (OSDI 2024), which demonstrated 4.48x goodput improvement over co-located systems and 20x reduction in latency variance between phases.

Figure 2: benefits of disaggregation

Economic Impact and Business Case

Traditional monolithic LLM serving creates significant inefficiencies through over-provisioning of high-end GPUs for decode phases and underutilization during prefill-heavy workloads. This leads to energy consumption from underutilized accelerators and management overhead for complex deployments.

Disaggregated architectures address these challenges through optimized hardware allocation, delivering 15-40% reduction in total infrastructure costs and 40-60% improvement in GPU utilization across workload phases. Energy efficiency improvements include 50% power consumption reduction through model compression and optimized allocation, with some deployments achieving up to 4x cost reduction in server sizing.

This also plays a pivotal role in adjusting the computation needs per use case, even within the same organization for different applications, as seen in Figure 3:

Figure 3: LLM workload patterns

Implementation Strategy: From Planning to Production

Successful implementation of disaggregated LLM serving begins with a clear, technical understanding of the infrastructure, systematic planning, and dynamic workload management. This section presents a blueprint for organizations aiming to optimize LLM inference efficiency while maximizing cost savings and resource utilization.

Architectural Blueprint: Disaggregating the Serving Pipeline

Disaggregated serving architectures physically and logically separate the two distinct computational phases of LLM inference:

• Prefill Cluster: Dedicated to the initial context processing. Typically utilizes high-performance, compute-optimized GPUs such as NVIDIA H100, which excel at tensor operations and batching large prompts for maximum throughput.
• Decode Cluster: Responsible for iterative token generation. This phase is memory-bound and benefits from GPUs with high memory bandwidth and low-latency cache access, such as NVIDIA A100, or emerging accelerators with advanced memory hierarchies.

Specialized hardware clusters are interconnected with low-latency, high-bandwidth networks (e.g., InfiniBand, NVLink) to efficiently pass key/value cache data between prefill and decode stages. Central workload orchestrators or GPU-aware schedulers (e.g., Kubernetes with custom scheduling logic or Ray) route requests dynamically based on size, type, and latency requirements.

Technical Steps for Implementation

1. Workload Profiling: Begin by rigorously profiling existing LLM deployments to distinguish prefill- and decode-heavy applications. Summarization and document-processing workloads tend to be prefill-dominant, while conversational agents and agentic systems lean towards decode-heavy, memory-bound operations.
2. Resource Segmentation and Mapping:

• Assign compute-intensive tasks (large context, batch generation) to clusters with maximum FLOPs and efficient batching support.
• Allocate memory-bound, token-wise generation workloads to clusters optimized for bandwidth, cache locality, and low-latency response (often with more nodes for distributed decode parallelism).

1. Framework Selection:

• vLLM: Broad model support, excellent for general-purpose deployments with continuous batching and PagedAttention for cache management.
• SGLang: High-throughput serving with RadixAttention, ideal for structured generation and multi-modal workloads.
• TensorRT-LLM: For large enterprises, offers robust integration, vendor support, and fine control over low-level GPU utilization.

1. Deployment Strategies:

• Parallel Deployment: Operate legacy and disaggregated architectures concurrently. Use load balancers to steer select traffic to the new setup for A/B testing, benchmarking, and staged production migration.
• Gradual Migration: Start with non-critical workflows, validate key metrics (latency, throughput, GPU utilization), then incrementally transition mission-critical applications once reliability is established.

1. State Management and Scalability: Use distributed cache systems (e.g., Redis, Memcached) to synchronize context and token state between clusters. Emphasize stateless microservices wherever possible to simplify failover, auto-scaling, and component restarts.

Real-World GPU Usage Patterns

1. Splitwise (Microsoft Research)

This paper presents extensive characterization on NVIDIA A100 and H100 GPUs using production traces. Splitwise achieves 1.4x higher throughput at 20% lower cost, or 2.35x more throughput with the same cost and power budge.

Hardware Results:

• Implemented on DGX-A100 and DGX-H100 virtual machines on Microsoft Azure with InfiniBand connectivity
• Shows that A100s can be more cost- and power-efficient for the token phase compared to H100s
• Demonstrates KV-cache transfer latency of around 8ms for A100 and 5ms for H100 setups

2. SGLang with DeepSeek Implementation

A real-world large-scale deployment running on 12 nodes in Atlas Cloud, each equipped with 8 H100 GPUs, achieving 52.3k input tokens per second and 22.3k output tokens per second per node for 2000-token input sequences.

Hardware Performance: This represents the first open-source implementation to nearly match the throughput reported in the official DeepSeek blog at large scale, with a cost of $0.20/1M output tokens compared to the official DeepSeek Chat API. The optimized strategy improves output throughput by up to 5x compared to vanilla tensor parallelism.

3. DistServe (OSDI 2024)

This paper introduces “DistServe,” which disaggregates prefill and decoding computation onto different GPUs. The system demonstrates significant performance improvements: it can serve 7.4x more requests or achieve 12.6x tighter SLO compared to state-of-the-art systems while staying within latency constraints for >90% of requests.

Hardware Implementation: The system was evaluated on A100-80GB GPUs with synthetic workloads of inputs of length 512 and output length 64. The paper shows that with proper placement, KV cache transfer overhead can be minimized to less than the time of a decoding step, thanks to high-speed networks like NVLink and PCIe 5.0.

Best Practices for Production

• Implement robust monitoring: Track cluster-level GPU utilization, power consumption, concurrency, token latency, and cache hit/miss rates to dynamically scale clusters.
• Ensure component isolation and redundancy: Disaggregated microservices reduce the risk of system-wide failures and allow for rapid component restart or horizontal scaling.
• Secure inter-component channels: Use service mesh frameworks and end-to-end encryption between clusters to protect sensitive user and model data during network transfer.

Security and Reliability in Distributed Architectures

Disaggregated architectures introduce new security considerations through increased attack surfaces and network communication requirements, but also provide benefits through component isolation and improved fault detection. Organizations must implement end-to-end encryption for inter-component communication, use service mesh architectures for secure service-to-service communication, and establish comprehensive access controls.

Reliability improves through component isolation that reduces cascade failure risks and enables faster recovery through component-level restart capabilities. High availability strategies include redundancy across both prefill and decode clusters, load balancing across healthy components, and circuit breakers to prevent cascade failures. State management requires distributed caching strategies with consistency guarantees and stateless architectures where possible to simplify recovery.

Future Outlook: Hardware and Software Evolution

The hardware landscape is evolving toward purpose-built chips optimized for disaggregated workloads, with memory-compute co-design for improved efficiency and specialized interconnects for distributed inference. Chiplet-based designs will enable flexible resource allocation, while near-memory computing reduces data movement overhead.

Software frameworks continue advancing with multi-modal model support for vision-language applications, model-specific disaggregation strategies, and dynamic resource allocation based on real-time workload analysis. Industry standardization efforts focus on common APIs for disaggregated serving frameworks, standardized metrics and benchmarking methodologies, and portable model formats optimized for disaggregated deployment.

The ecosystem development includes vendor-neutral orchestration platforms, integrated development and deployment tools, and community-driven optimization libraries that will accelerate adoption across organizations of all sizes.

Conclusion

Disaggregated serving represents a fundamental shift in LLM infrastructure design, addressing the inherent inefficiencies of monolithic architectures through specialized optimization of prefill and decode phases. With proven benefits across performance, cost, and operational metrics, the technology has matured from academic research to production-ready implementations adopted by major organizations.

As hardware and software continue evolving to support disaggregated workloads, this approach will become the standard for large-scale LLM deployment, enabling organizations to deliver AI services more efficiently and cost-effectively than ever before.

About the Author

Anat Heilper

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