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Tokens, Embeddings and Logits

Trace token IDs, embedding rows and next-token scores. Work out which tensors grow with a batch, when all-position logits waste memory, and why token probabilities do not establish factual confidence.

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LESSONS THAT TEACH THIS

Foundations of AI InfrastructureA request becomes tokens, and tokens become two different workloads30 min→From Model to ServiceBuild retrieval as a budgeted pipeline22 min→From Model to ServiceGuided lab: run a reproducible model baseline30 min→

PRACTICE THIS IN REAL QUESTIONS

Open-Weights Models & Serving EnginesYour new model deployment produces fluent answers that score badly on evaluations. Where do you look?→LLM Inference & ServingA customer sends the same prompt twice at temperature zero and gets different answers. Explain why, and what you can promise them.→Open-Weights Models & Serving EnginesThe open-weights model you are deploying is multimodal. What changes about serving it?→AI Infrastructure System DesignDesign token-level billing and metering for an LLM API: count at the engine, aggregate, reconcile, and survive the failure modes.→Coding for InfraImplement sampling from a model's logits with temperature, top-k and top-p. What are the numerical traps?→CUDA, Triton & Kernel EngineeringSelect the top k logits from a 128,000-token vocabulary on the GPU. What shape does the kernel take and why not just sort?→