클라이언트: CoreWeave, Inc.
양식: 백서
크기: 2.7MB
언어: 영어
날짜: 10.06.2026
CoreWeave and VAST Quant Whitepaper: Re-Engineering Quant Infrastructure for Deterministic AI Research
As AI expands what quant teams can test and retrain, infrastructure variance is becoming a direct tax on alpha generation. In this whitepaper, CoreWeave and VAST Data outline a coordinated architecture blueprint purpose-built for AI-scale quant research, without sacrificing reproducibility, data coherence, or governance confidence.
This white paper covers:
This white paper covers:
- Why incremental hybridization adds coordination overhead faster than it adds research capacity
- The three conditions that have to hold simultaneously: deterministic compute, unified data, and verifiable execution
- How to evaluate whether your environment behaves as one coordinated system or a collection of loosely connected layers
- How a coordinated data layer eliminates staging delays, dataset duplication, and GPU idle time
- Six checks to assess your infrastructure before your next scaling decision
