同时，越早期推出的机型端侧模型越小，整体 ROM 包大小相应减小。但博主强调，实际功能差异不会有看到的包大小差异那么大，绝大部分都是依据芯片能耗比部署的端侧模型大小的差异。
Sarvam 105B shows strong, balanced performance across core capabilities including mathematics, coding, knowledge, and instruction following. It achieves 98.6 on Math500, matching the top models in the comparison, and 71.7 on LiveCodeBench v6, outperforming most competitors on real-world coding tasks. On knowledge benchmarks, it scores 90.6 on MMLU and 81.7 on MMLU Pro, remaining competitive with frontier-class systems. With 84.8 on IF Eval, the model demonstrates a well-rounded capability profile across the major workloads expected of modern language models.
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Iterative Intersection: For every subsequent cell, the algorithm checks if its usages intersect with the current Dirty Set.
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