TLDR: TyphoonMLA is a novel attention kernel for Large Language Models (LLMs) that combines two existing Multi-Head Latent Attention (MLA) implementations, “naive” and “absorb,” to significantly speed up inference, especially when dealing with shared prefixes like system prompts. By intelligently applying the computationally efficient naive method to shared parts of the data and the memory-efficient absorb method to non-shared parts, TyphoonMLA achieves up to 3.24x higher throughput on GPUs and NPUs with minimal memory overhead, without sacrificing accuracy.
Large Language Models (LLMs) have become indispensable across many applications, from powering chat assistants to acting as coding agents. However, their immense computational demands often lead to slow inference, impacting user experience and increasing operational costs. Addressing these efficiency challenges is crucial for their widespread and sustainable deployment.
A key innovation in improving LLM inference efficiency is Multi-Head Latent Attention (MLA), an attention mechanism found in advanced LLMs like DeepSeek-v3 and Kimi K2. MLA introduces a clever way to store contextual information (the KV-cache) in a compact, low-rank latent space, which helps overcome memory bottlenecks in attention layers.
MLA offers two distinct ways to implement its core calculations: the ‘naive’ and ‘absorb’ formulations. The naive approach is generally favored during model training and the initial ‘prefill’ stage because it’s computationally efficient. However, for the ‘decode’ stage (when the LLM generates tokens one by one), existing kernels typically use the absorb method. This is because absorb minimizes the use of High Bandwidth Memory (HBM), which is often a bottleneck during decoding.
The challenge with the absorb method is that it tends to be ‘compute-bound,’ meaning its performance is limited by processing power rather than memory access. This limitation prevents it from fully benefiting from data reuse opportunities, especially when LLMs process ‘shared prefixes.’ Shared prefixes are common in many scenarios: for instance, a system prompt that guides an LLM’s behavior is often shared across many user queries. Other examples include parallel reasoning techniques (like Tree-of-Thought) or speculative decoding, where multiple queries attend to the same initial sequence of tokens.
While techniques exist to exploit shared prefixes in older attention architectures like MHA and GQA, they don’t directly apply to MLA because MLA’s decode stage is compute-bound. This means current MLA kernels can’t fully capitalize on the efficiency gains offered by shared data.
Introducing TyphoonMLA: A Hybrid Solution
This is where TyphoonMLA comes in. Researchers have introduced TyphoonMLA, a novel hybrid approach that intelligently combines the strengths of both naive and absorb MLA formulations. The core idea is to apply the naive formulation to the parts of attention calculations that benefit most from shared prefixes (the compute-bound regions), while using the absorb formulation for the non-shared parts to keep memory bandwidth requirements low.
Think of it like this: for the shared, common parts of a query, TyphoonMLA uses the naive method, which is computationally more efficient when there’s a lot of data reuse. For the unique, non-shared parts of each query, it switches to the absorb method, which is better at saving memory bandwidth. This dynamic switching allows TyphoonMLA to maximize efficiency across different computational demands.
TyphoonMLA also includes a clever ‘fall-back’ mechanism. At very small batch sizes, where there isn’t enough data reuse to make the naive approach beneficial, it automatically reverts to an absorb-only kernel, ensuring consistent high performance.
Also Read:
- PPSD: Boosting LLM Inference Speed with Pipelined Self-Speculative Decoding
- InfiniPipe: A New Approach for Efficient Long-Context LLM Training
Performance and Impact
The results are impressive. TyphoonMLA significantly boosts the throughput of attention calculations in MLA architectures, achieving speedups of up to 3 times on NPUs and 3.24 times on GPUs. This performance gain comes with a minimal memory footprint increase, only about 3% in HBM size. The benefits are particularly pronounced with longer system prompts, as these increase the amount of shared data that TyphoonMLA can optimize.
Crucially, TyphoonMLA produces outputs identical to standard MLA implementations, meaning there’s no loss in accuracy and no need for additional training or fine-tuning. It’s also designed to be compatible with existing optimization techniques like PagedAttention and various parallelization strategies, making it easy to integrate into current LLM inference frameworks.
This innovation offers a practical and effective solution to enhance the performance and efficiency of LLM inference, ultimately leading to a better user experience and reduced operational costs for LLM applications. The code for TyphoonMLA is open-sourced and publicly available for the community to explore and utilize. You can find more details in the full research paper: TYPHOONMLA: A MIXEDNAIVE-ABSORBMLA KERNELFORSHAREDPREFIX.
