Why LLMs Aren’t Deterministic (Even at Temperature 0) – And How to Fix It скачать в хорошем качестве

Why LLMs Aren’t Deterministic (Even at Temperature 0) – And How to Fix It

why large language models (LLMs) like ChatGPT give you slightly different answers even when the settings are fixed at temperature = 0? Shouldn’t that make them fully deterministic? In this video, we break down one of the most confusing topics in AI: LLM inference nondeterminism. You’ll learn: Why the usual “concurrency + floating point” explanation doesn’t tell the full story. The real culprit behind changing outputs in inference servers. How batch size and parallel requests sneak nondeterminism into your results. What researchers are doing to defeat nondeterminism and achieve true reproducibility. This isn’t just about math—it’s about making AI reliable for science, engineering, and production. Stay tuned until the end, because that’s where we uncover the key strategy for batch invariance that could change the way we think about LLM inference forever. Join my AI newsletter: https://upaspro.com/newsletter/ More information: https://upaspro.com/why-llms-arent-de... 👇 Timestamps: 00:00- Introduction 01:10- Channel welcome 01:41- Problem of Non-deterministic LLM 03:57- Actual cause of LLM variation 09:21- Outro #AI #LLM #MachineLearning #DeepLearning #Reproducibility #ChatGPT #Inference