AI Training vs Inference Explained
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AI training and AI inference are two completely different challenges, and most explanations only cover the training side. Running a model for millions of people is the part that gets skipped.
In this video we use Llama 3.1 405B to show what really happens behind the scenes. We cover how pre-training pushes trillions of tokens through a 405 billion parameter model across tens of thousands of GPUs, why post-training is the quieter phase that gives us checkpoints like Opus 4.5, 4.6 and 4.7 off the same base model, and the part almost nobody explains clearly: inference at scale.
There is a reason a single large context request can occupy multiple GPUs before it returns even one token. We get into why, and what it means for every chat and agentic workflow you run.
#AI #AITraining #AIInference #LLM #MachineLearning #DeepLearning #ArtificialIntelligence #LargeLanguageModels #Llama3 #GPU #MLOps #AIEngineering #NeuralNetworks #kodekloud
AI training and AI inference are two completely different challenges, and most explanations only cover the training side. Running a model for millions of people is the part that gets skipped.
In this video we use Llama 3.1 405B to show what really happens behind the scenes. We cover how pre-training pushes trillions of tokens through a 405 billion parameter model across tens of thousands of GPUs, why post-training is the quieter phase that gives us checkpoints like Opus 4.5, 4.6 and 4.7 off the same base model, and the part almost nobody explains clearly: inference at scale.
There is a reason a single large context request can occupy multiple GPUs before it returns even one token. We get into why, and what it means for every chat and agentic workflow you run.
#AI #AITraining #AIInference #LLM #MachineLearning #DeepLearning #ArtificialIntelligence #LargeLanguageModels #Llama3 #GPU #MLOps #AIEngineering #NeuralNetworks #kodekloud
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