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This article details a specific inference optimization technique for Small Language Models (SLMs) that prioritizes batching requests by token length rather than processing them individually. By sorting sequences to minimize padding waste, this approach significantly improves memory bandwidth efficiency on both GPU and CPU hardware without altering the model architecture. The findings offer practical guidance for deploying SLMs in narrow automation tasks where throughput is critical.
Batching by Length Instead of Looping Item by Item for SLM Optimization
We finish off our short series on SLM optimization with the third entry, focused on batching by length instead of looping item by item.
Previous articles in this series discussed constraining output space as well as reusing the prompt prefix with a key-value cache, both framed as approaches to small language model (SLM) narrow automation optimization. Let's finish this series off with the third entry, focused on batching by length instead of looping item by item. As in our previous articles, all benchmarks below use Qwen2.5-0.5B-Instruct in float16 through Hugging Face Transformers, running on an M2 Macbook Air with 24GB RAM and a 16-core Neural Engine. We will continue to use the support ticket framing from our first article. Processing one ticket per forward pass is the single largest source of waste…
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