Sparse MLA only attends to the top-K tokens, so the rest of the KV need not live on the GPU. Hybrid HiSparse in vLLM builds on that, and a request keeps decoding after its KV stops fitting in HBM. It keeps KV on the GPU while there is room. Under pressure a request releases its coldest pages to host memory, keeps a small hot buffer of what the indexer asks for, and keeps decoding instead of being preempted. 📊 Demonstrated on GLM 5.3, one 8× H200 node, full 1M context. Same host memory, configured concurrency 32: KV offloading kept 5-6 requests running. Hybrid HiSparse kept 19-25. 🔹 Hot pages are ordinary KV blocks from the same pool (Hybrid Memory Allocator) 🔹 One fused kernel resolves resident, hot and missing rows, CUDA-graph capturable 🔹 Prefix caching, OffloadingConnector, P/D imports and MTP keep working Built by @RedHat_AI and @PrimeIntellect with the vLLM community. Planned for v0.30; pinned commit, flags and calculator are in the post👇 🔗