Xiaomi MiMo v2.5 Inference Optimization: How Hybrid SWA Pushes LLM Efficiency to Its Limits

Xiaomi’s MiMo v2.5 is presented as a 310-billion-parameter sparse Mixture of Experts model that activates 15 billion parameters per token and combines this design with Hybrid Sliding Window Attention to reduce inference costs. Its attention architecture uses five local 128-token sliding-window layers for every full-attention layer, aiming to retain long-range context while cutting KV-cache storage to roughly one-seventh, or about six times less than comparable full-attention models. Learnable attention-sink bias is intended to preserve important early-context tokens within local-attention layers. Xiaomi reportedly achieved output speeds above 1,000 tokens per second on a one-trillion-parameter variant through additional inference optimization, with potential benefits including lower memory requirements, greater user concurrency, faster prefill, and reduced API or self-hosting costs. The discussion positions MiMo v2.5 as suitable for cost-sensitive tasks such as summarization, classification, translation, and basic question answering, while recommending more capable frontier models for complex reasoning, advanced code generation, and nuanced creative work.

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