Equation 7 · The Hardest Unsolved Problems in Small and On-Device AI
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A quantization scheme can post a close to zero while is large, provided the loss concentrates on one or a few tasks that are a small share of the suite. Nothing about being small implies is small; the two only converge if degradation is spread evenly, and the studies below find that it is not.
Sources cited in the article section
- [2] A Case Study of Selected PTQ Baselines for Reasoning LLMs on Ascend NPU ↗
- [3] Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression ↗
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