AIQuail and the case for specialized AI-SQL inference
A user reports that a query took 6.84 hours with hand-tuned vLLM, versus a 15-minute “speed-of-light” estimate.
TLDR
A user reports substantial host overhead on several queries run through hand-tuned vLLM baselines. They say vLLM also discarded cached data it later needed, causing 50 million extra tokens of processing. One query took 6.84 hours, compared with a 15-minute “speed-of-light” estimate. The user argues that AI-SQL warrants specialized inference: nearly all requests are known up front, allowing better cache planning, and the priority is finishing the entire query quickly rather than serving interactive requests. They say those differences motivated Quail, a specialized inference engine.
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Quail and the case for specialized AI-SQL inference
A user reports that a query took 6.84 hours with hand-tuned vLLM, versus a 15-minute “speed-of-light” estimate.
TLDR
A user reports substantial host overhead on several queries run through hand-tuned vLLM baselines. They say vLLM also discarded cached data it later needed, causing 50 million extra tokens of processing. One query took 6.84 hours, compared with a 15-minute “speed-of-light” estimate. The user argues that AI-SQL warrants specialized inference: nearly all requests are known up front, allowing better cache planning, and the priority is finishing the entire query quickly rather than serving interactive requests. They say those differences motivated Quail, a specialized inference engine.