VQV Signal
LLM outputs vary across GPUs due to floating-point non-associativity and hardware-dependent kernel selection during inference. Different matrix-multiplication kernels cause nondeterministic results despite identical models and prompts.
LLM outputs vary across GPUs due to floating-point non-associativity and hardware-dependent kernel selection during inference. Different matrix-multiplication kernels cause nondeterministic results despite identical models and prompts.
AI-assisted summary based on the listed source.
Large language model (LLM) outputs are expected to be reproducible under greedy decoding, yet in practice the same model, prompt, and software stack produce different outputs on different GPUs. The root cause is floating-point non-associativity combined with hardware-dependent kernel selection. Inference...
Reproducibility is critical for reliable LLM deployment and debugging. Understanding and mitigating these hardware-induced variations improves consistency across diverse GPU platforms.
Hardware and robotics watchers may want to track whether this becomes a product, benchmark, or deployment signal.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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