Unverified50% confidenceOpinionExact time
Even with optimizations such as quantization, distillation and caching, the marginal cost of inference remains hard to bring close to zero.
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50%
Confidence
Long-term
Relevance
10/20/2026
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VerifiedIf a low-cost model has insufficient reasoning capability, it leads to more retry rounds, and the hidden costs of accumulated failed retries may exceed those of a high-cost, high-efficiency solution (the paradox of "cheap models actually being more expensive").75% similarUnverifiedMean rather than median should be used to estimate on-device throughput, because the median filters out slow but real use cases, making capacity estimates overly optimistic.75% similarUnverifiedWhen the marginal cost of generation and execution approaches zero, what becomes scarce is no longer the speed of writing code, but the ability to turn vague requirements into acceptable tasks, and the judgment about the output.75% similarUnverifiedIn tasks lacking repetitive patterns, such as free-form creative writing and open-ended question answering, the hit rate is low, and the speedup benefit drops significantly, sometimes even incurring slight overhead due to verification costs.75% similarUnverifiedQuantized models and sparse computation paths are sensitive to the numerical distribution of inputs; if only zero tensors are used during warmup, quantization operators may follow different code paths than those taken during real inference, causing memory estimation distortion.74% similar
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