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Xiaomi’s MiMo-V3 to adopt new architecture as HySparse2 cuts long-context costs
Xiaomi's MiMo-V3 will implement a new architecture using HySparse2 technology, reducing costs associated with long-context inference and enhancing retrieval for agentic tasks. HySparse2 achieves significant reductions in computation and cache size, leading to improved performance metrics for various benchmarks.
Key points
- Xiaomi's MiMo-V3 will utilize a new architecture.
- The new core technology, HySparse2, aims to lower long-context inference costs.
- HySparse2 reduces prefill computation by 5.02 times at a 1 million token context.
- It also decreases the key-value cache by 4.5 times.
- Performance metrics show higher MRCRv2 and RULER-v2 scores.
- AgentPPL and LongPPL results are lower due to these optimizations.
Why it matters
- The development of high-efficiency AI models aligns with 2025 IT trends focused on cost-effective AI computation.
- HySparse2 reflects the growing demand for scalable solutions in natural language processing as tasks become more complex.
- Similar advancements in AI architecture have been seen with companies like Google and OpenAI investing in efficiency.
Xiaomi's MiMo-V3, leveraging HySparse2 technology, represents a significant advancement in AI efficiency, emphasizing the industry's trend towards optimizing performance for large context tasks.