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Explore Turbo4 Datatype Deep Dive — 9x Storage Reduction Without a Full-Precision Copy with 2,002 views, 70 likes, and 0 comments. Experience the impact of this video content that has captured audience attention.

One datatype flag. Zero rescoring copy. 9x smaller vectors. This video breaks down Qdrant 1.19's Turbo4 datatype — built on Google Research's TurboQuant algorithm — and benchmarks exactly what you gain and what you give up when you drop the full-precision rescoring copy entirely. In this video you'll learn how to: 🔹 Understand why traditional quantization secretly costs 36 bits/dim, not 4 — because it keeps a full FP32 copy on disk for rescoring 🔹 Configure Qdrant's new datatype=models.Datatype.TURBO4 in one line of Python 🔹 See how randomized orthogonal rotation + 16-level quantization compresses vectors without collapsing their geometry 🔹 Benchmark Recall@K, latency, and QPS across FP32 / Two-Copy Quant+Rescore / Turbo4 head-to-head on real data 🔹 Read the actual trade-off: ~9x storage cut and ~10x more QPS vs. a real recall hit at standalone Top-10 🔹 Recover full recall with a 2-stage pipeline — Turbo4 candidate retrieval (K=50) + cross-encoder re-ranking 🔹 Decide when to reach for Turbo4 (huge, disk-constrained, multi-tenant, edge) vs. when to stick with max-recall configs NOTEBOOK Get the notebook used in this video: https://colab.research.google.com/drive/1YR6YDOa5p4RFwWJIieILag90XHKNuzGc?usp=sharing&utm_source=hidevsyoutube&utm_medium=video&utm_campaign=turbo4_deep_dive&utm_content=notebook QDRANT DOCS REFERENCED IN THIS VIDEO Vector Datatypes: https://qdrant.tech/documentation/concepts/vectors/?utm_source=hidevsyoutube&utm_medium=video&utm_campaign=turbo4_deep_dive&utm_content=datatypes#datatypes Quantization Guide: https://qdrant.tech/documentation/guides/quantization/?utm_source=hidevsyoutube&utm_medium=video&utm_campaign=turbo4_deep_dive&utm_content=quantization Qdrant Python Client: https://github.com/qdrant/qdrant-client?utm_source=hidevsyoutube&utm_medium=video&utm_campaign=turbo4_deep_dive&utm_content=github_client MORE QDRANT Qdrant homepage: https://qdrant.tech/?utm_source=hidevsyoutube&utm_medium=video&utm_campaign=turbo4_deep_dive&utm_content=homepage Qdrant GitHub: https://github.com/qdrant/qdrant?utm_source=hidevsyoutube&utm_medium=video&utm_campaign=turbo4_deep_dive&utm_content=github #Qdrant #VectorDatabase #VectorSearch #Turbo4 #Quantization #RAG #AIEngineering #MachineLearning

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