PAT: Accelerating LLM Decoding via P refix- A ware A t tention with Resource Efficient Multi-Tile Kernel

Key findings from this study

• The study found that decode-phase attention represents a memory-bandwidth bottleneck exacerbated by repeated loading of identical prefix KV cache entries across concurrent requests.
• The researchers demonstrate that prefix-aware kernel optimization combined with dynamic multi-tile execution reduces memory transfers and pipeline stalls arising from uneven KV sequence lengths.
• The authors report that hierarchical prefix sharing, common in system prompts and RAG-augmented requests, can be exploited through modified attention kernel design to improve decode throughput.

Overview

Decode-phase attention in large language model serving constitutes a memory-bound bottleneck due to extensive key-value cache retrieval from global memory. Real-world LLM workloads contain hierarchical shared prefixes across concurrent requests, such as system prompts and retrieval-augmented generation templates. Existing attention kernel implementations underutilize this structural redundancy. One-query-per-compute-thread-array designs repeatedly transfer identical prefix KV cache entries, while uniform tiling strategies leave on-chip resources underutilized and produce pipeline stalls when KV sequence lengths vary. PAT (Prefix-Aware Attention) addresses these limitations through prefix-aware kernel optimization with resource-efficient multi-tile execution.

Methods and approach

The proposed approach redesigns decode attention kernels to exploit hierarchical prefix sharing across requests. PAT employs prefix-aware computation that identifies and reuses shared KV cache blocks, reducing redundant memory transfers during concurrent request processing. Multi-tile kernel execution adapts on-chip resource allocation dynamically based on actual KV sequence length distributions. This design reduces memory bandwidth pressure and minimizes pipeline stalls that arise from heterogeneous sequence lengths in practical batched serving scenarios.

Results

The study demonstrates that PAT reduces memory bandwidth consumption during decode attention through prefix-aware KV cache reuse. Multi-tile kernel execution allocates on-chip resources efficiently, matching resource utilization to observed sequence length heterogeneity rather than worst-case scenarios. The optimization pipeline decreases decode attention latency by exploiting the hierarchical prefix structure prevalent in real-world LLM service workloads, thereby improving overall LLM serving throughput.

Implications

PAT advances practical LLM inference efficiency by targeting the memory-bound characteristics of production serving workloads. The framework benefits any LLM deployment that processes heterogeneous request batches with shared prefixes, including systems employing system prompts, template-based tools, or retrieval-augmented generation. Adoption of prefix-aware kernel designs may become standard practice for inference optimization when workload characteristics permit exploitation of shared prompt structure.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.