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SparseAccelerate: Efficient Long-Context Inference for Mid-Range GPUs

James Vo

TL;DR

SparseAccelerate tackles the quadratic attention bottleneck in long-context LLM inference on mid-range GPUs by introducing dynamic sparse attention patterns that adapt to input characteristics. The method relies on a kernel-aware optimization framework and three sparsity patterns—Triangular, Interval-Slash, and Block-Cluster—to efficiently compute attention during a two-phase inference (prefill and decode). It demonstrates up to a 1.04x TTFT reduction at 32K tokens and substantial memory savings, enabling 16K–128K token contexts on dual NVIDIA A5000 GPUs. By reducing attention complexity and providing scalable memory efficiency, SparseAccelerate enables real-time, large-context applications (e.g., RAG, long-form QA) on accessible hardware, with future work aiming to broaden sparsity patterns and accelerate search heuristics.

Abstract

As Large Language Models (LLMs) scale to longer context windows, the computational cost of attention mechanisms, which traditionally grows quadratically with input length, presents a critical challenge for real-time and memory-constrained deployments. Existing sparse attention techniques have sought to reduce this complexity, but they often incur significant overhead or compromise accuracy, making them less practical for large contexts on mid-range hardware. In this paper, we introduce SparseAccelerate, a dynamic sparse attention method that adapts its sparsity patterns based on input characteristics, effectively flattening the attention complexity curve. Our approach is effective for input lengths starting at 16K tokens and scales efficiently up to 128K tokens on dual NVIDIA A5000 GPUs (24GB each). Experimental results show that SparseAccelerate achieves up to a 1.04x reduction in Time-To-First-Token (TTFT) latency at 32K tokens, while also providing substantial memory savings. These improvements yield practical gains for memory-intensive applications and long-context tasks that were previously infeasible with standard attention. Beyond latency reductions, SparseAccelerate fundamentally shifts the scaling trend, demonstrating the smallest TTFT growth gradient relative to context length among competing methods. Ongoing evaluations on diverse benchmarks confirm its scalability, positioning SparseAccelerate as a critical advancement toward efficient, real-time, and large-context LLM inference on accessible hardware.

SparseAccelerate: Efficient Long-Context Inference for Mid-Range GPUs

TL;DR

SparseAccelerate tackles the quadratic attention bottleneck in long-context LLM inference on mid-range GPUs by introducing dynamic sparse attention patterns that adapt to input characteristics. The method relies on a kernel-aware optimization framework and three sparsity patterns—Triangular, Interval-Slash, and Block-Cluster—to efficiently compute attention during a two-phase inference (prefill and decode). It demonstrates up to a 1.04x TTFT reduction at 32K tokens and substantial memory savings, enabling 16K–128K token contexts on dual NVIDIA A5000 GPUs. By reducing attention complexity and providing scalable memory efficiency, SparseAccelerate enables real-time, large-context applications (e.g., RAG, long-form QA) on accessible hardware, with future work aiming to broaden sparsity patterns and accelerate search heuristics.

Abstract

As Large Language Models (LLMs) scale to longer context windows, the computational cost of attention mechanisms, which traditionally grows quadratically with input length, presents a critical challenge for real-time and memory-constrained deployments. Existing sparse attention techniques have sought to reduce this complexity, but they often incur significant overhead or compromise accuracy, making them less practical for large contexts on mid-range hardware. In this paper, we introduce SparseAccelerate, a dynamic sparse attention method that adapts its sparsity patterns based on input characteristics, effectively flattening the attention complexity curve. Our approach is effective for input lengths starting at 16K tokens and scales efficiently up to 128K tokens on dual NVIDIA A5000 GPUs (24GB each). Experimental results show that SparseAccelerate achieves up to a 1.04x reduction in Time-To-First-Token (TTFT) latency at 32K tokens, while also providing substantial memory savings. These improvements yield practical gains for memory-intensive applications and long-context tasks that were previously infeasible with standard attention. Beyond latency reductions, SparseAccelerate fundamentally shifts the scaling trend, demonstrating the smallest TTFT growth gradient relative to context length among competing methods. Ongoing evaluations on diverse benchmarks confirm its scalability, positioning SparseAccelerate as a critical advancement toward efficient, real-time, and large-context LLM inference on accessible hardware.

Paper Structure

This paper contains 25 sections, 2 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Proposed Method
  3. Kernel-Aware Sparse Pattern Search Algorithm
  4. Vertical-Slash Sparse Attention Algorithm
  5. Block-Sparse Attention Algorithm
  6. Main Idea
  7. Limitations and Trade-Offs
  8. Effectiveness Threshold
  9. Experimental Results
  10. Latency Reduction and Effectiveness Threshold
  11. Small Context Lengths (10--100 Tokens)
  12. Medium Context Lengths (500--4000 Tokens)
  13. Large Context Lengths (8000--32000 Tokens)
  14. Very Large Context Lengths (64,000--128,000 Tokens)
  15. Overall Performance Trade-Offs
  16. ...and 10 more sections

Figures (2)

  • Figure 1: Inference Latency (TTFT) Across Different Attention Methods.
  • Figure 2: GPU memory usage Across Different Attention Methods.