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A popular technique to make AI more efficient has drawbacks
One of the most widely used techniques to make AI models more efficient, quantization, has limits — and the industry could be fast approaching them. In
In the context of AI, quantization refers to lowering the number of bits — the smallest units a computer can process — needed to represent information. That could spell bad news for AI companies training extremely large models (known to improve answer quality) and then quantizing them in an effort to make them less expensive to serve. Evidence suggests that scaling up eventually provides diminishing returns; Anthropic and Google reportedly recently trained enormous models that fell short of internal benchmark expectations.
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