Inference Optimization for MiMo v2.5: Mastering Hybrid SWA Efficiency

Short summary

MiMo v2.5 combines Stochastic Weight Averaging with gradient-driven pruning and mixed 8/16-bit quantization to cut model size by 60% while retaining 98%+ accuracy. The approach achieves 3x lower inference latency on mobile GPUs and 45% lower memory usage. Best suited for edge devices and real-time pipelines, but not recommended for full-precision or latency-insensitive batch workloads.

• •Hybrid SWA with adaptive weighting, latency-aware pruning, and mixed quantization reduces model size 60%
• •Achieves 3x inference speedup on mobile GPUs with <1% accuracy loss
• •Best for edge devices and real-time pipelines; not for full-precision batch processing

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