每日简报:AI传播虚假疾病信息引发关注

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The AOT path is the production path and the more powerful of the two. AITune profiles all backends, validates correctness automatically, and serializes the best one as a .ait artifact — compile once, with zero warmup on every redeploy. This is something torch.compile alone does not give you. Pipelines are also fully supported: each submodule gets tuned independently, meaning different components of a single pipeline can end up on different backends depending on what benchmarks fastest for each. AOT tuning detects the batch axis and dynamic axes (axes that change shape independently of batch size, such as sequence length in LLMs), allows picking modules to tune, supports mixing different backends in the same model or pipeline, and allows you to pick a tuning strategy such as best throughput for the whole process or per-module. AOT also supports caching — meaning a previously tuned artifact does not need to be rebuilt on subsequent runs, only loaded from disk.

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再看看这个视角还能带来什么。既然行星一半处于黑暗,或许我们也能标示这点:

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