In the rapidly evolving world of artificial intelligence, large language models (LLMs) have emerged as powerful tools capable of generating human-like text, answering complex questions, and even assisting in knowledge work. At the heart of their impressive capabilities lies a mechanism called "attention." While attention layers have been a revolutionary breakthrough for LLMs, they also come with significant bottlenecks in computational speed and memory usage. Two new and impactful architectural implementations seek to solve some problems, even despite the persistence of some interesting kinds of “bottlenecks” in memory and speed.

The Breakthroughs

Two architectures have recently emerged as major players in optimizing attention: Meta's variation of Ring Attention, introduced in their new Scout and Maverick architecture, and DeepSeek-V3's Multi-head Latent Attention (MLA).

• Llama 4 Blog
• DeepSeek-V3 paper

Llama 4’s Ring Attention utilizes chunking, similar to sliding-window attention, to facilitate attention in a ring-like manner. This approach reduces the number of computations and is inherently parallel, allowing attention to be performed more linearly. It has a complexity of O(N), where N is the sequence length plus the number of chunks.

DeepSeek-V3’s Multi-head Latent Attention (MLA) compresses the keys and values to reduce the size of the Key-Value (KV) cache. Instead of storing a full matrix of keys and values, they are compressed into a lower-rank vector that can be stored more efficiently. While this performs comparably to Multi-Head Attention, it still exhibits overall quadratic memory and space complexity O(N²), where N is the sequence length.

These breakthroughs primarily address two significant challenges with modern attention mechanisms: attention's quadratic complexity and the KV cache's memory complexity.

The Bottlenecks

Despite their benefits, these new attention mechanisms still share shortcomings with their optimized predecessors. Scaling the sequence length remains very difficult, even with linear computational complexity. This limitation is mainly imposed by memory and hardware capabilities, which remain significant bottlenecks. While compressing the KV cache reduces its memory footprint, the cache size can still be challenging to store and the quadratic complexity of Multi-Head Attention persists for certain aspects.

Both methodologies offer promising advancements in optimizing attention. However, they continue to face some of the same fundamental bottlenecks that attention layers have always encountered regarding memory footprint and computational complexity. Though we are on the path to more efficient and optimal attention layers, the next breakthrough in AI will be realizing that maybe attention isn’t all you need.

While attention was a much-needed breakthrough, at QWERKY AI we understand the difficulties bottlenecks like these still pose, and are working towards a future where attention is simply one more stop on a clearer path forward.