FlashMemory-DeepSeek-V4: Ultra-Long Context via Lookahead Sparse Attention
Original: FlashMemory-DeepSeek-V4: Lightning Index Ultra-Long Context via Lookahead Sparse Attention
FlashMemory-DeepSeek-V4 cuts KV cache to 13.5% of full-context baseline at 500K-token scale without accuracy loss.
FlashMemory-DeepSeek-V4 introduces Lookahead Sparse Attention (LSA), a predictive inference paradigm that retains only query-critical KV chunks in GPU memory instead of the full cache. A Neural Memory Indexer, trained independently using a backbone-free dual-encoder strategy, proactively forecasts which historical tokens will matter next. The system compresses average KV cache footprint by 86.5% and exceeds 90% compression at 500K-token scales, while delivering a slight accuracy gain of +0.6% on long-context benchmarks.
FlashMemory-DeepSeek-V4 (FM-DS-V4) is a research system targeting one of the most acute bottlenecks in long-context LLM deployment: the explosive GPU memory consumption driven by the key-value (KV) cache during inference. In standard autoregressive decoding, every generated token must attend over the full history of cached key-value pairs. At extreme context lengths—such as 500K tokens—this makes full-cache serving economically or physically infeasible on most GPU hardware, while sparse approximations have historically introduced significant accuracy degradation.
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