Long-Context LLM Inference Cost Breakdown: Architecture Optimization from Prefill to KV Cache

Abstract

When a model needs to process code repositories, terminal output, or Agent interaction histories spanning hundreds of thousands of tokens, the main pressure during inference typically does not come from a single Decode step. Instead, it comes from the full computation of Prefill and the KV Cache that continuously expands with context length. As foundation models increasingly gain native visual understanding capabilities, this problem becomes more pronounced: multimodal inputs further lengthen prompts and increase cache footprint.

This article analyzes inference costs in long-context and Agent scenarios, focusing on two optimization axes: Prefill computation and Global KV Cache. It examines design approaches including Causal Encoder-Decoder structures, cross-layer KV reuse, low-precision caching, and knowledge decoupling from four angles—computational complexity, memory footprint, cache structure, and knowledge capacity—and explains how these optimizations affect complex reasoning, software engineering, and long-horizon Agent tasks in real engineering scenarios. For developers concerned with inference performance, memory planning, and Agent application deployment, this content serves as a reference framework for understanding the architectural evolution of next-generation large models.

1. Why Long-Context Inference Is More Expensive Than It Seems

In a typical single-turn Q&A, user input may only be a few hundred to a few thousand tokens, and inference latency and memory usage are usually acceptable. But once a model enters an Agent workflow, context scale changes by orders of magnitude.

For example, a complex software engineering task may involve:

• Hundreds of tool calls;
• Hundreds of thousands of tokens of terminal output;
• Complete code repositories and file diffs;
• Task state records spanning long cross-session histories.

All of this content must remain in the model context to ensure consistency in subsequent decisions. However, traditional Decoder-only Transformer architectures face two types of cost simultaneously when handling such scenarios.

1.1 Prefill Computation Cost

In a Decoder-only architecture, the model has L layers and the prompt length is N. The Prefill stage typically requires every layer to process the full prompt in order to generate the KV states needed for subsequent Decode. Its main computational complexity can be approximated as:

O(N × L)

When N grows from a few thousand to hundreds of thousands or even millions, Prefill latency scales almost linearly. Especially in high-concurrency online services, the Prefill of a single long prompt can block subsequent requests, causing queueing latency to rise significantly.

1.2 KV Cache Memory and Transfer Cost

Traditional models typically store independent Key and Value for each layer. As context grows longer, KV Cache continuously occupies GPU HBM. For multi-turn sessions or distributed inference, these caches also need to be transferred between Host Memory, SSD, and the network, further increasing system complexity.

Therefore, the optimization goal cannot stop at “enabling the model to handle longer contexts.” It must also consider:

• The storage size of each KV entry;
• The number of historical positions that actually participate in attention;
• Whether redundant storage exists across different Transformer layers;
• The number of network layers that actually need to be fully executed during Prefill.

These objectives together determine the model’s inference efficiency in long-context tasks.

2. Core Optimization Directions: Compressing Both Computation and Cache

When facing the above problems, one straightforward approach is to rely solely on model quantization to reduce weight and activation overhead, or solely on sparse attention to reduce computation. But from a system perspective, if only one part is optimized, the other part may still become the bottleneck.

For example, if only KV Cache is compressed, Prefill computation remains heavy; if only Prefill latency is reduced, KV Cache still occupies large amounts of HBM, limiting concurrency. Therefore, a more reasonable approach is to co-optimize computation and cache.

2.1 Asymmetric Computation: Avoiding Full Layer Execution in Prefill

In traditional architectures, Prefill and Decode usually share the same network depth. That is, every token in the prompt must pass through the full L layers of computation to generate subsequent caches.

A new design approach introduces a Causal Encoder-Decoder structure. The Encoder here is not a completely independent dual-tower structure; instead, it forms an asymmetric computation path within the backbone network:

• Most prompt tokens primarily pass through the first half of the network during Prefill;
• The subsequent Decode stage still uses the full network;
• The number of activated parameters per token during Prefill is significantly lower than during Decode.

For example, in a MoE model with 552B backbone parameters, the activated parameter count per token during Decode is 16B; during Prefill, through the above structure, the activated parameter count can be reduced to 8B. This can nearly halve Prefill computation without noticeably sacrificing representation capability.

The value of this approach lies in the fact that it does not forcibly reduce prompt length, but rather lowers the effective computation depth that prompt tokens pass through during Prefill. For the many intermediate tool return results in Agent scenarios, such content often does not need to go through the same full computation path as high-value reasoning tokens.

2.2 Cross-Layer KV Reuse: Reducing Redundant Cache and Index Computation

In traditional Transformers, each layer typically stores its own KV. For models with many layers, this creates substantial redundant information. In particular, Key and Value in adjacent layers are usually highly correlated, and storing complete copies is not always necessary.

The optimization approach can be summarized in two points:

1. Reuse Main KV across layers;
2. Reuse Indexer K and Top-K selection results.

This means the model does not need to recompute and cache all KV at every layer, but can reduce redundant cache and index computation through sharing mechanisms. At the same time, attention selection across different layers can also reuse partial results, further reducing computational overhead.

This optimization is especially important in long-context scenarios, because the total size of KV Cache depends not only on per-token size but also on the number of layers and the number of tokens. Cross-layer reuse can directly reduce redundancy along the layer dimension.

2.3 Low-Precision Caching: Finding the Balance Between Memory and Precision

Beyond reducing the number of KV entries, it is also possible to reduce the byte width of each KV entry. Low-precision caching typically uses 4-bit or similar bit-width representations to store Key and Value, significantly reducing memory footprint.

As a concrete example, in traditional schemes, Global KV Cache may reach:

3514 Bytes / token

With cross-layer reuse combined with low-precision caching, this can be compressed to:

890 Bytes / token

About one quarter of the original. For a 1M-token context, this means the KV Cache resident in GPU HBM can drop significantly from the multi-GB level, allowing more requests to share the same accelerator card or leaving memory headroom for longer sessions.

It is important to note that low-precision caching does not mean unconditionally reducing model precision. In practice, systems typically need calibration strategies, error control, or layer-wise precision allocation to ensure that attention computation quality does not noticeably degrade due to cache compression. For developers, such optimizations are better implemented uniformly at the inference engine level, so that the application side does not need to be aware of specific bit-width details.

3. Decoupling Knowledge Capacity from the Reasoning Pipeline

In long-context tasks, the model not only needs to process large inputs but also needs to invoke long-term knowledge and skills during reasoning. Traditional approaches typically compress all knowledge into backbone network parameters, which means knowledge expansion is often accompanied by parameter growth and increased training costs.

Another approach introduces Engram-like mechanisms: decoupling knowledge memory from backbone computation to expand the model’s knowledge capacity with lower activation overhead. Simply put, the backbone network focuses on the expressive capability of the reasoning pipeline, while knowledge memory can be stored and retrieved in a scalable manner.

The benefits of this design include:

• The model can expand its knowledge scope without significantly increasing per-token activation computation;
• Knowledge updates can be more flexible, without always requiring full retraining;
• Tool results, historical decisions, and external knowledge in Agent workflows can be organized and reused more efficiently.

From a system perspective, this decoupling also helps reduce unnecessary knowledge redundancy in KV Cache. Because frequently accessed long-term facts and decision states can be managed through independent mechanisms rather than all being stacked in layer-by-layer KV.

4. Implications for Complex Reasoning and Agent Tasks

The above optimizations are not isolated architectural experiments but engineering choices oriented toward real workloads. In complex reasoning tasks, the model may need to:

• Maintain long-chain reasoning states;
• Repeatedly revisit earlier intermediate conclusions;
• Dynamically adjust subsequent plans based on tool output.

These behaviors all require context management to be as lightweight as possible while not losing critical information. By reducing Prefill computation and compressing KV Cache, the model can carry longer effective contexts under the same hardware conditions and reduce end-to-end latency in long tasks.

For example, in a software engineering Agent, a single task often requires multiple model calls. Each call involves new prompt assembly, insertion of tool return results, and state continuation. With optimized cache mechanisms, the system can significantly reduce repeated Prefill pressure and allocate limited HBM more to active context rather than having it exhausted by historical KV.

5. Engineering Considerations for Developers

Although most of the above optimizations occur inside model architectures and inference engines, there are still some actionable principles for application developers.

5.1 Control “Effective Context” Rather Than Blindly Pursuing Maximum Length

Even if a model supports 1M tokens, it does not mean any task should use the longest context. A more reasonable approach is to:

• Periodically compress intermediate states that are no longer needed;
• Keep only high-value history and necessary tool results in context;
• Structurally summarize verbose output before feeding it back to the model.

This reduces Prefill burden while also limiting KV Cache growth.

5.2 Pay Attention to Cache Reuse in Multi-Turn Sessions

In Agent scenarios, consecutive multi-turn calls typically share large historical prefixes. If the inference service can reuse existing KV Cache rather than re-Prefilling every turn, overall latency will drop significantly. The application side can try to maintain context stability across requests and reduce unnecessary prefix changes.

5.3 Treat Long-Context Processing as a System Problem, Not a Single Model Capability

What truly determines long-horizon Agent performance is not just model context length. It also includes:

• Retrieval and reranking strategies;
• How tool return results are organized;
• Session state management;
• Batching and scheduling strategies for inference services.

Only by combining model capabilities with system architecture can a balance between cost and effectiveness be achieved.

6. Summary

Inference costs in long-context and Agent scenarios mainly come from Prefill computation and KV Cache footprint. Traditional Decoder-only architectures face obvious linear growth pressure in both aspects.

Current optimization directions worth attention include:

1. Asymmetric Prefill computation: reducing the effective network depth that prompt tokens pass through;
2. Cross-layer KV reuse: eliminating redundant caching across different layers;
3. Low-precision caching: reducing the KV byte width per token;
4. Knowledge decoupling: separating long-term knowledge from backbone computation.

These designs all point toward one goal: enabling the model to maintain complex reasoning capabilities while keeping the overhead of long contexts within an engineering-feasible range. For developers, understanding the motivations and benefits behind these optimizations helps make more robust technical judgments when evaluating models, designing Agent systems, and planning inference resources.