Quail: Speed up AI-SQL by jointly optimizing query planner and inference engine
A query like the one above might produce millions of sequences of thousands of tokens — RIP your token budget. These queries often require much less than frontier intelligence, so small open-weights models can crush. But naïvely delivering these sequences directly to an inference engine optimized for agentic inference through interfaces for arbitrary user-controlled requests is inherently and massively inefficient.So we built an inference engine to fix this: the QUery-Aware Inference Layer (Quail).
- ▪A query like the one above might produce millions of sequences of thousands of tokens — RIP your token budget.
- ▪These queries often require much less than frontier intelligence, so small open-weights models can crush.
- ▪But naïvely delivering these sequences directly to an inference engine optimized for agentic inference through interfaces for arbitrary user-controlled requests is inherently and massively inefficient.So we built an inference engine to fix
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| Original publisher | Modal |
| Canonical URL | https://modal.com/blog/quail-billion-tpm |
| Publication time | Tue, 29 Sep 2026 23:19:52 +0000 |
| Retrieval time | 2026-09-29T23:22:33.986Z |
| Last seen | 2026-09-29T23:22:33.986Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | sDbQgos6CpXk · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
All posts Back Research September 24, 2026 •15 minute read Quail: Speeding up AI-SQL by jointly optimizing query planner and inference engine Charles Frye Member of Technical Staff @charles_irl Shreya Shankar Asst Professor, CMU FSD Lab @sh_reya I see it as a point on the LLM pareto optimal curve in a regime that had a large revealed latent demand (no thinking, single token, low latency acceptable intelligence) that was under-invested into because of a race to higher intelligence.- Karpathy-san, on JevWhile everyone and their cousin is loudly building coding agents and chatbots, there’s a quieter inference revolution going on in the backend.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Modal.
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