黄仁勋涉婚外情 发布致英伟达全员信
华尔街见闻
登上热搜！黄仁勋会因为绯闻辞职吗？英伟达给AI交易“雪上加霜”
华尔街见闻英伟达CEO黄仁勋周日发出全员公开信，正式承认婚外情并道歉称对妻子“不忠”，公司董事会同日宣布启动独立调查。Yorkville Ives分析师警告，调查结果可能导致黄仁勋被免职，将给股价带来风险。事件发生之际，AI股已因多重压力持续承压，英伟达夜盘跌超3%，公司面临的不确定性进一步上升。
事关AI交易走向，硅谷、华尔街热议“黄仁勋绯闻”，马斯克力挺、特朗普也发声
华尔街见闻黄仁勋婚外情事件持续发酵，红杉合伙人Maguire力挺黄仁勋，马斯克转发称“我同意”。Bill Ackman表示，黄仁勋是人，都会犯错。特朗普称愿带黄仁勋出访亚洲。散户看戏表示，“贝佐斯有过婚外情，比尔·盖茨有过婚外情，马斯克也有过婚外情”，但都没影响公司。但华尔街担心黄仁勋会辞职，“很多基金经理将在未来几周减持英伟达”。
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I'm not convinced the AI bubble exists
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The Silent Hyperparameter: Quantifying the Impact of Inference Backends on LLM Reproducibility
arXiv cs.LGarXiv:2605.19537v3 Announce Type: replace
Abstract: Progress in LLMs is increasingly measured through standardized benchmarks, where state-of-the-art improvements are often separated by fractions of a percentage point. At the same time, the computational cost of evaluating modern LLMs has driven widespread adoption of specialized inference backends, software systems that execute trained models efficiently at inference time. While critical for scalability, system-level optimizations, such as custom CUDA kernels and reduced-precision arithmetic, can alter token probabilities and introduce non-determinism, possibly cascading into divergent generation. In this work, we first survey the inference landscape, identifying $200$ distinct engines, and analyze $35,000$ ML publications, finding that the specific inference stack is rarely reported despite this widespread diversity. We then present a systematic empirical study of how inference backends affect LLM benchmark results. Holding model weights, decoding parameters, and hardware constant, we evaluate five widely used inference engines, including vLLM, SGLang, and llama.cpp, across multiple open-weight models spanning 4B-30B parameters and established benchmarks. We show that the choice of backend alone can shift benchmark scores by up to $16.6$ percentage points and induce high rates of output disagreement. By isolating backend optimizations and tracing the execution pipeline, we find this divergence is driven by system-level optimizations such as prefix caching and CUDA graphs, custom kernels, and engine-specific defaults in logit processing. Our findings identify the inference backend as a previously unreported yet consequential hyperparameter in LLM evaluations and advocate standardized reporting of inference stacks to improve the reproducibility and interpretability of benchmark comparisons.
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