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by spindump8930
49 days ago
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Remember that models on different inference platforms might not necessarily give exactly the same results, adding another axis of non-determinism to development. Things like quantization, custom model serving silicon, batching, or other inference optimizations might mean a model from the original provider performs differently from the hosted one :/ This paper isn't the exact same scenario, since it's an auditable open weight llama model, but shows the symptoms of this: https://arxiv.org/pdf/2410.20247 |
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Model performance consistency is important not because you want inference determinism (which you can actually get by setting tempetature to zero and applying a static seed). The `another axis of non-determinism` can be illustrated by the question "if I move from openrouter to bedrock, will gpt-5.5 perform the same?", to which the answer is no, at least not necessarily.
This is important because workflows that used to work on one platform might degrade or outright not work on another, even using the same model, which you have to account when deciding which provider to use.