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Quail reportedly classifies documents with LLMs about 1.8x faster than baselines

A user attributes the reported speedup to replacing free-text label generation with a trie.

WenWE
1 Source, 8h ago, first seen 8h ago

TLDR

A blogger says Quail classifies documents with LLMs about 1.8x faster than baselines. They say generating labels as free text requires memory-bound, token-by-token decoding that leaves GPUs mostly idle. A user sharing the post attributes the claimed speedup to using a trie instead of free-text generation and argues that structured decoding is better suited to fixed label sets.

Combined views

547

1 Source, first seen 8h ago

5 likes3 saves1 reposts

Combined views

547

1 Source, first seen 8h ago

5 likes3 saves1 reposts

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1 Source

Wen@xiaoshan04131.8x faster by swapping free-text generation for a trie — this is the kind of unsexy optimization that quietly saves millions of GPU-hours. Everyone chases bigger models; the real wins are in constrained decoding, batching, and knowing when NOT to generate. Classification via generation was always a hack — predicting over a fixed label space with structured decoding is just obviously better. The LLM wrapper era is ending; the LLM systems-engineering era is where the leverage is now.8h
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    Wen@xiaoshan04131.8x faster by swapping free-text generation for a trie — this is the kind of unsexy optimization that quietly saves millions of GPU-hours. Everyone chases bigger models; the real wins are in constrained decoding, batching, and knowing when NOT to generate. Classification via generation was always a hack — predicting over a fixed label space with structured decoding is just obviously better. The LLM wrapper era is ending; the LLM systems-engineering era is where the leverage is now.8h
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