Unverified50% confidenceTradeoffExact time
Quantized 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.
1
Sources
50%
Confidence
Long-term
Relevance
10/1/2026
First Seen
Sources
Related Entities
Related Claims
Unverified量化模型的困惑度可能仅上升0.1-0.5,但在特定下游任务(如代码生成、数学推理)上的表现可能出现显著退化80% similarVerifiedThe non-determinism of large language models stems from their autoregressive sampling process; even with temperature set to 0, differences in inference frameworks, batch sizes, and hardware floating-point operations can all cause output drift.77% similarUnverifiedSetting the sampling temperature to 0 causes reasoning models to fall into a thinking loop and become stuck during the thinking phase.75% similarVerifiedEven when the temperature is set to 0, some inference frameworks still have non-determinism in underlying floating-point computations, causing output differences across hardware and across batches.75% similarUnverifiedOne of the main risks of implementing inference from scratch is numerical drift, where precision issues or incorrect computation order at any step may be amplified after propagating through multiple layers.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/964610API
curl https://kongchang.com/api/v1/knowledge/claims/964610MCP
get_claim(id=964610)