Rumor

Xiaomi to adopt HySparse architecture to reduce KV cache memory footprint in upcoming AI models

Wednesday, February 4, 2026 at 03:00 PM

Xiaomi is implementing HySparse, a hybrid attention architecture, for its upcoming frontier AI model. The architecture reduces the KV cache memory footprint by having sparse attention layers reuse the KV cache of full attention layers, which optimizes memory utilization for long-context processing. This technical shift has implications for high-density DRAM and SSD demand in AI infrastructure.

Context

Xiaomi is preparing to deploy a new architectural framework called HySparse in its next-generation frontier AI models to drastically lower memory costs. The HySparse system employs a hybrid attention mechanism that interleaves full attention layers with sparse ones, allowing the latter to reuse the Key-Value (KV) cache of the former. This eliminates redundant computations and significantly reduces the memory footprint, facilitating "cheap" long-context capabilities for complex AI tasks without the typical linear scaling of hardware requirements. Evaluated on 7B dense and 80B Mixture of Experts (MoE) models, the architecture enables broader adoption of high-parameter AI across consumer devices. Industry analysts view this as a bullish signal for the DRAM and SSD markets. By making extreme context lengths computationally feasible, Xiaomi is accelerating a shift toward massive, persistent memory states that require high-capacity storage tiers to manage offloaded context data, ultimately driving demand for advanced semiconductor hardware in the AI supply chain.

Related Companies

Xiaomi

1810