StreamIndex: Memory-Bounded Compressed Sparse Attention via Streaming Top-k

TL;DR

StreamIndex enables memory-efficient, scalable sparse attention for long sequences by streaming top-k selection, significantly extending GPU memory limits with minimal loss in accuracy.

Contribution

We introduce StreamIndex, a chunked streaming top-k driver that avoids full intermediate materialization, enabling sparse attention on sequences over a million tokens.

Findings

Runs at sequence length 1,048,576 with 6.21 GB peak HBM, 32x larger than previous limits.

Achieves near-perfect recall (≥0.9980) across multiple design configurations.

Performs attention computations on 262,144-length sequences in under 2 seconds with 18.56 GB peak memory.

Abstract

DeepSeek-V3.2 and V4 introduce Compressed Sparse Attention (CSA): a lightning indexer (a learned scoring projection over compressed keys) scores them, the top-k are selected per query, and a sparse attention kernel reads only those. Public CSA implementations materialize a [B, S, H_I, T] FP32 score tensor before the top-k reduction. With H_I=64 indexer heads and the V4-Flash compression ratio m=4, that intermediate is 256 GB at sequence length S=65,536, exceeding any single-GPU high-bandwidth-memory (HBM) budget. We present StreamIndex, a Triton implementation of the CSA pipeline whose central component is a chunked partition-merge top-k driver that never materializes the full intermediate. On synthetic-but-realistic V4-shaped inputs at the indexer-step (layer) level on a single NVIDIA H200, the materialize path runs out of memory (OOMs) at S=65,536 with V4-Flash dimensions; StreamIndex runs the same indexer to S=1,048,576 with 6.21 GB peak HBM, a 32x regime extension. Set-overlap recall against the materialize ground truth is bit-exact at small S where both fit; across three 5-point design-space sweeps (chunk size, key-tile size, top-k), mean recall rounds to 1.0000 with min recall at least 0.9980 in every cell. The chunked driver composes with TileLang's pipelined attention kernel: at S=262,144 with V4-Flash dimensions, the materialize indexer paired with TileLang attention OOMs while the chunked indexer paired with the same attention runs in 1.97 s at 18.56 GB peak. Our contribution targets the indexer step; we make no claim of a faster attention kernel or of real-checkpoint end-to-end behavior. Code: https://github.com/RightNow-AI/StreamIndex.