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Group work deliberately, with shapes and request identity intact.
Batching introduces a relationship between examples that would otherwise be processed separately. These guides examine what that relationship means for shapes, padding, useful usage records and operational decisions. Begin with the tensor fundamentals article to understand the leading batch axis.
Then look at budgeting and inference to consider how a group of requests should be assembled and measured. Document whether each output still maps to a specific input, how incompatible examples are handled and which measurements belong to the entire batch. Compare like-for-like tasks before concluding that a larger batch improves the workflow you care about.
2 articles tagged Batching
Make LLM consumption predictable with a budget for each task. Separate context limits from output reserves, choose batching and cache policies, and connect estimates, retries, and reported usage to useful completion.
7 min readA tensor contract needs more than an array of numbers. Learn how to name axes, validate numerical types, preserve feature meaning, and batch compatible examples without hiding errors at the API boundary.
7 min read
TensorAPI.com
