SWA Bounded Replay
A prefill optimization technique for model layers that use sliding window attention. Instead of computing the entire window from the start, it replays only the short window at the end of the prompt, substantially reducing prefill work.
Timeline (last 90 days)
DeepInfer integrates mechanisms such as DeepSpark speculative decoding with consistency scheduling verification, SWA bounded replay, asynchronous zero-overhead scheduling, and structured output.
SWA bounded replay means replaying or retaining a bounded length of historical context outside the window under the sliding window attention mechanism, in order to maintain long-context consistency within a limited memory and compute budget.
Some of DeepSeek's model layers use sliding window attention, attending only to the most recent 128 tokens; SWA bounded replay replays only a short window at the end of the prompt, which is said to nearly eliminate total prefill work.
All Facts (3)
DeepInfer integrates mechanisms such as DeepSpark speculative decoding with consistency scheduling verification, SWA bounded replay, asynchronous zero-overhead scheduling, and structured output.
50%UnverifiedSWA bounded replay means replaying or retaining a bounded length of historical context outside the window under the sliding window attention mechanism, in order to maintain long-context consistency within a limited memory and compute budget.
50%UnverifiedSome of DeepSeek's model layers use sliding window attention, attending only to the most recent 128 tokens; SWA bounded replay replays only a short window at the end of the prompt, which is said to nearly eliminate total prefill work.
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