To process variable-length inputs more efficiently, tokenize them, group inputs with similar token counts, and batch within each group. Each batch then needs padding only up to its own longest sequence, rather than the longest sequence in a mixed-length batch. This can reduce wasted padding work and let the model process multiple examples together—but the best batch size and bucket boundaries depend on your workload and hardware.
Why batch by length instead of looping item by item?
A one-item-at-a-time loop runs a separate forward pass for every input. Batching lets the model process several examples together, which can improve throughput by making better use of the hardware. But batched tensors generally need compatible dimensions. For variable-length sequences, shorter inputs are padded to match the longest input in the batch.
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That means a batch containing one very long sequence and several short ones can spend computation on many padding tokens. Length-based batching addresses this by grouping similarly sized inputs before forming batches. Microsoft’s Bucket Sequence Batcher documentation describes sorting sequences into buckets and batching within each bucket to reduce padding cost. PyTorch Serve’s Model Inference Optimization Checklist also recommends sequence bucketing as a possible way to reduce unnecessary padding.
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How length-bucketed batching works
- Determine each input’s model length. Tokenize the text with the model’s tokenizer, then record the resulting token count. Character counts are not a dependable substitute because tokenization determines the sequence length the model sees.
- Group inputs by length. Sort inputs by token count or assign them to length ranges. The boundaries should reflect the lengths in your workload, not a presumed universal standard.
- Form batches within groups. Set a maximum batch size, then collect similarly sized inputs together. Microsoft’s documentation shows configurable bucket boundaries and maximum batch size; those are configuration choices, not universal recommendations.
- Pad each batch to its local maximum. A batch is padded to the length of its longest member. Bucketing limits how much shorter sequences in that batch need to be extended.
- Restore input order if needed. If downstream code expects outputs in the original order, keep each input’s original index and reorder results after inference.
How to choose buckets and batch size
There is no single best set of bucket boundaries or batch size. Wider buckets may fill more easily but can include more padding; narrower buckets can reduce padding while leaving batches less full or requiring more scheduling and bookkeeping. Increasing batch size may improve throughput, but it also increases memory demand and can be constrained by the longest sequence in the batch.
For offline work, you can sort a complete dataset and form batches from it, but sorting and restoring order add processing and operational overhead. In online serving, a system may collect pending requests over a window to build batches. Waiting can improve batch fill, but adds queueing delay. The size of that tradeoff depends on the request rate, latency target, and hardware; there is no workload-independent setting to recommend.
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Benchmark the alternatives on your workload
Measure the existing item-by-item path against ordinary mixed-length batching and length-bucketed batching. Sweep batch sizes and, where practical, bucket boundaries. A throughput improvement is a hypothesis to test, not a guarantee: PyTorch’s checklist says sequence bucketing “could potentially improve the throughput by 2X.” That conditional statement is not a promised result for a particular model or system.
Keep throughput and latency separate. Throughput measures work completed over time; per-request latency includes the time a particular input waits and runs. A pre-collected offline batch may improve throughput without reducing the response time for a live request.
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For results others can interpret, report:
- Model and numerical precision, tokenizer, and padding behavior.
- Hardware and software stack.
- Dataset size and token-length distribution.
- Batch size and bucket boundaries.
- Timing method, throughput, and latency.
- Peak memory use and any out-of-memory failures.
- Whether outputs agree with the reference implementation on representative inputs and edge cases.
Matthew Mayo’s September 25, 2026 KDnuggets example uses Qwen2.5-0.5B-Instruct in float16 with Hugging Face Transformers on an M2 MacBook Air with 24GB RAM. Mayo says the example processes the same 600 tickets in a fraction of the wall-clock time with the same predictions, and advises choosing batch size by measurement rather than intuition. That is a report about one example setup, not a controlled, general performance estimate; the available article information does not establish a verified numerical speedup that can be applied to other workloads. See Mayo’s article for that example.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check correctness as well as speed
Compare batched outputs with an unbatched reference for your actual task and implementation. Test ordinary inputs as well as edge cases, including unusually short and long sequences. Pay particular attention to attention masks, padding side, output indexing after reordering, and generated sequence lengths. Padding and batching can expose implementation mistakes even when a benchmark looks faster.
Mayo reports identical outputs for the example in his article and describes validation as essential. That is author-reported validation of that example, not independent replication or evidence that every batching implementation will preserve outputs.
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What to expect from the three approaches
| Approach | Padding and throughput | Latency, memory, and complexity |
|---|---|---|
| Item-by-item inference | Avoids padding between separate inputs, but runs a forward pass per input. No general throughput comparison is established. | No batch-fill waiting or input reordering is needed. Memory use and latency depend on the individual input and implementation. |
| Ordinary mixed-length batching | Processes multiple inputs together, but shorter sequences may be padded to the longest sequence in each batch. | Batch size and the longest sequence affect memory needs. The cited sources do not provide a controlled latency or memory comparison against the other approaches. |
| Length-bucketed batching | Groups similar lengths to limit avoidable padding; throughput may improve, but the result must be measured for the workload. | May require sorting, reordering, or waiting to fill online batches. Long sequences can still constrain a batch. No universal latency or safe memory limit is established. |
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