大模型为何聚焦降低注意力成本？2026年架构趋势深度解析

引言 | Introduction

近期，Sebastian Raschka 整理并发布了一份涵盖从2024年初至2026年春季发布的40多个开源大语言模型的架构图谱。这份图谱揭示了一个清晰且统一的行业趋势：几乎所有模型架构的创新努力，都指向同一个核心目标——让注意力机制变得更廉价、更快速、能够处理更长的序列，同时竭力维持模型性能。这引发了一个根本性问题：为何降低注意力成本成为了当前大模型架构设计的普遍焦点？

Recently, Sebastian Raschka compiled and released an architectural atlas covering over 40 open-source large language models released from early 2024 to spring 2026. This atlas reveals a clear and unified industry trend: almost all architectural innovations are directed towards the same core goal—making the attention mechanism cheaper, faster, and capable of handling longer sequences, while striving to maintain model performance. This raises a fundamental question: why has reducing attention costs become the universal focus in current LLM architectural design?

核心观察：收敛的设计语言与分裂的技术路线 | Core Observation: Convergent Design Language vs. Divergent Technical Paths

这份图谱最引人深思之处，并非某个模型采用了何种新奇技巧，而在于它清晰地展示：当前的大模型研发，实质是在一个非常狭窄的设计空间内进行反复探索与优化。一种“收敛的设计语言”正在形成。

The most thought-provoking aspect of this atlas is not any novel trick adopted by a particular model, but rather how it clearly demonstrates that current LLM development is essentially an iterative process of exploration and optimization within a very narrow design space. A "convergent design language" is emerging.

趋同的组件（Convergent Components）：诸如混合专家系统（MoE）、查询-键归一化（QK-Norm）、滑动窗口注意力（Sliding Window Attention）等技术，几乎已成为新一代模型的标配。
分化的实现（Divergent Implementations）：然而，在如何具体实现“高效注意力”这一目标上，各家方案却大相径庭。例如，将 Mamba 等状态空间模型与注意力层混合使用、用线性注意力完全替换部分标准注意力层、或采用 MLA 等技术压缩键值（KV）缓存——每条技术路线都是一场不同的赌注。

Convergent Components: Technologies like Mixture of Experts (MoE), Query-Key Normalization (QK-Norm), and Sliding Window Attention have almost become standard features in the new generation of models.

Divergent Implementations: However, approaches diverge significantly on how to specifically achieve "efficient attention." Examples include hybridizing attention layers with state-space models like Mamba, completely replacing some standard attention layers with linear attention, or compressing the Key-Value (KV) cache using techniques like MLA—each technical path represents a different bet.

根本瓶颈：长上下文推理的计算成本 | The Fundamental Bottleneck: Computational Cost of Long-Context Reasoning

这种普遍的“降本”趋势，其根源在于一个公认的瓶颈：长上下文推理所带来的难以承受的计算成本。标准的 Transformer 自注意力机制具有 O(n²) 的时间与空间复杂度，当序列长度（n）扩展到数十万乃至百万令牌级别时，其计算和内存需求将变得极其昂贵，甚至不可行。

This widespread trend of "cost reduction" stems from a recognized bottleneck: the prohibitive computational cost of long-context reasoning. The standard Transformer self-attention mechanism has O(n²) time and space complexity. When the sequence length (n) scales to hundreds of thousands or even millions of tokens, its computational and memory demands become extremely expensive, if not infeasible.

因此，2026年左右的模型架构图谱中，出现了明显的 “混合架构”（Hybrid Architecture） 趋势。模型不再纯粹依赖标准注意力，而是引入其他计算范式来分担压力：

Consequently, a clear trend towards "Hybrid Architecture" emerged in the architectural atlas around 2026. Models no longer rely purely on standard attention but introduce other computational paradigms to share the load:

Qwen3.5：采用 3:1 的 DeltaNet 层与普通注意力层交替堆叠。
Kimi Linear：将大部分注意力层替换为线性注意力版本，仅保留四分之一的 MLA 层。
NVIDIA Nemotron 3 Nano：策略更为激进，使用 Mamba-2 处理大部分层，仅让注意力机制在关键网络节点出现。

Qwen3.5: Employs a 3:1 alternating stack of DeltaNet layers and standard attention layers.

Kimi Linear: Replaces most attention layers with linear attention variants, retaining only a quarter of MLA layers.

NVIDIA Nemotron 3 Nano: Takes a more radical approach, using Mamba-2 for most layers and allowing the attention mechanism to appear only at critical network nodes.

这些方案的共同前提是承认：让标准注意力机制全程处理超长序列是不现实的，必须为其寻找高效的“替代品”或“辅助者”。当前的技术分歧，本质上是对不同替代方案可靠性、效率与能力权衡的探索。

The common premise of these solutions is the acknowledgment that it is impractical for the standard attention mechanism to handle ultra-long sequences end-to-end; efficient "replacements" or "assistants" must be found for it. The current technical divergence essentially explores the trade-offs in reliability, efficiency, and capability among different alternative schemes.

工程化深水区：从宏观创新到微观优化 | The Deep End of Engineering: From Macro Innovation to Micro-Optimization

另一个值得关注的细节是 QK-Norm 的迅速普及。从 Qwen3 开始，几乎所有新模型都加入了这一归一化层，无论是稠密模型还是 MoE 模型。OLMo 2 甚至将其整个规范化方案从前置归一化（pre-norm）改为后置归一化（post-norm），以配合 QK-Norm 来稳定训练过程。

Another noteworthy detail is the rapid proliferation of QK-Norm. Starting with Qwen3, almost all new models have incorporated this normalization layer, be it dense or MoE models. OLMo 2 even switched its entire normalization scheme from pre-norm to post-norm to work in concert with QK-Norm for stabilizing training.

这深刻地说明，大模型训练已进入 “微操”阶段。像 Transformer 本身那样的架构级重大创新已多年未见，当前的竞争更多地体现在各种“小技巧”的叠加效应上。归一化层放置的位置、旋转位置编码（RoPE）的维度设置、MoE 中专家路由的稀疏度调控——这些以往可能被忽视的细节，如今已成为影响模型性能与训练稳定性的关键因素。

This profoundly indicates that LLM training has entered a phase of "micro-optimization." Major architectural innovations like the Transformer itself have not been seen for years. Current competition increasingly revolves around the compound effects of various "small tricks." The placement of normalization layers, the dimensionality settings of Rotary Position Embedding (RoPE), the sparsity tuning of expert routing in MoE—these details, which might have been overlooked in the past, have now become critical factors influencing model performance and training stability.

Step 3.5 Flash 是一个有趣的特例。它通过在训练和推理阶段均使用多令牌预测（MTP-3）来保持高吞吐量。其总参数量为196B，激活参数量为11B，推理速度却能与参数量超过600B的 DeepSeek V3 相媲美。有人认为这是“取巧”，但这更像是一种务实的工程哲学：当架构层面的创新空间受限时，便在工程实现与算法协同设计上寻找突破口。

Step 3.5 Flash is an interesting exception. It maintains high throughput during both training and inference by employing Multi-Token Prediction (MTP-3). With a total parameter count of 196B and an activated parameter count of 11B, its inference speed rivals that of DeepSeek V3, which has over 600B parameters. Some view this as a "clever trick," but it resembles more of a pragmatic engineering philosophy: when innovation space at the architectural level is constrained, seek breakthroughs in engineering implementation and algorithm co-design.

结论：从范式革命到增量优化 | Conclusion: From Paradigm Shift to Incremental Optimization

这份图谱收录了从 3B 到 1T 参数的众多模型，详细标注了其关键设计选择、发布日期和配置文件链接。然而，其真正价值不在于这些信息本身，而在于它让我们清晰地认识到：大语言模型的架构演进，正从“范式革命”阶段滑向“增量优化”阶段。

This atlas includes numerous models ranging from 3B to 1T parameters, meticulously annotated with their key design choices, release dates, and configuration file links. However, its true value lies not in this information per se, but in how it makes us clearly realize: the architectural evolution of LLMs is sliding from a phase of "paradigm shift" into one of "incremental optimization."

下一个突破点将出现在哪里？它可能不在于注意力机制本身的根本性改变，而在于如何更智能地集成注意力、状态空间模型、线性变换等现有组件。或者，它可能需要研究者彻底跳出当前的框架，寻找一种全新的序列建模范式。

Where will the next breakthrough emerge? It may not lie in a fundamental change to the attention mechanism itself, but in how to more intelligently integrate existing components like attention, state-space models, and linear transformations. Alternatively, it may require researchers to leap entirely out of the current framework and discover a completely new paradigm for sequence modeling.

图谱链接（Atlas Link）: sebastianraschka.com/llm-architecture-gallery/

常见问题（FAQ）

为什么所有大模型都在想办法降低注意力机制的成本？

因为标准注意力机制在处理长序列时计算复杂度呈平方级增长，成本过高。行业共识是必须寻找更高效的替代方案来处理长上下文推理。

当前大模型架构设计出现了什么明显的趋势？

形成了“收敛的设计语言”——大家都在优化注意力成本，但技术路线分化。混合架构成为主流，用状态空间模型、线性注意力等辅助或替代标准注意力层。

从技术演进角度看，大模型发展正处于什么阶段？

正从宏观的范式革命进入微观的增量优化阶段。工程化进入深水区，焦点从架构创新转向对注意力机制等核心组件的精细化成本优化。