HySparse2 cuts long-context costs through KV reuse
Published on September 23, 2026, the post describes two levels of KV sharing to reduce prefill FLOPs and cache size, with better retrieval scores than MiMo-V2.6's Hybrid SWA at a context length of 1 million tokens.
Source: HySparse2 usa compartilhamento de KV em inferência com contexto longo (arxiv.org). Text prepared with AI from this source.
What happened and what to do
Published on September 23, 2026, the post presents HySparse2, which shares KV across decoders and reuses KV between sparse and full-attention layers. According to the post, this design reduces prefill FLOPs and KV cache size, while achieving better retrieval scores than MiMo-V2.6's Hybrid SWA architecture at 1 million tokens, in agentic workloads with growing context.
A company running long-context inference can evaluate these techniques with benchmarks based on its actual models and workloads. It can implement metrics collection for latency, memory use, prefill cost, and retrieval quality, compare cache strategies, and display results in dashboards to guide infrastructure changes.
How the consultancy can help
Wendelmaques can diagnose long-context inference costs and bottlenecks, scope a benchmark, and implement the metrics collection, comparative tests, and dashboards needed.
Next step
Send a short description of your inference case or long-context costs to receive a scoped proposal.
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I reply with scope and a proposal.