Many teams hit a wall when they try to run large language models on long inputs. The core attention mechanism grows quadratically with context length, turning a 32‑k token window into a costly bottleneck that stalls training, inflates inference latency, and limits the size of documents the model can understand. Engineers end up either truncating valuable context or investing in expensive hardware that still cannot keep up with the O(N²) demand.
MiniMax Sparse Attention (MSA) offers a direct fix. It splits attention into two lightweight stages. First, an Index Branch picks a fixed number of key‑value blocks for each query using a learned Top‑k selection that works at block granularity (default 128 tokens). Because the selection is shared inside each GQA group, the overhead stays tiny. Second, a Main Branch runs exact softmax only on the selected blocks, guaranteeing that every query still sees its immediate neighborhood plus a controllable set of long‑range tokens.
The result is a per‑query cost of O(k·Bₖ) instead of O(N). With k = 16 and Bₖ = 128, each query attends to just 2 048 tokens regardless of whether the total context is 8 k, 32 k, or 128 k tokens. This turns the quadratic wall into a flat line, delivering up to 5× speed‑up on the index kernel and 3.7× over competing sparse selectors on SM100 GPUs. The method plugs straight into existing Grouped Query Attention checkpoints; a short warm‑up phase trains the indexer via a KL alignment loss, after which the model can be fine‑tuned or continued from a dense checkpoint.
For practitioners, MSA means longer documents fit into memory, faster response times for chatbots and agents, and lower cloud bills without sacrificing the quality scores seen on MMLU, GSM8K, or HumanEval benchmarks. It gives a clear path to scale context length while keeping the compute budget predictable.
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