GOOGLE JUST SHRINK 31GB OF AI MEMORY DOWN TO 4GB

@whitewhoadie

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Promoted product

turbovec

A vector index built on TurboQuant, written in Rust with Python bindings - RyanCodrai/turbovec

Why it worked

The video leverages a surprising and impressive technical feat (shrinking AI memory by 7x) presented in a concise and engaging manner, with a clear call to action to explore the open-source project.

Summary

The video introduces TurboVec, an open-source vector index developed by Google Research. It highlights how TurboVec can compress 31GB of AI memory down to 4GB with minimal performance sacrifice.

Structure

1Introduction of TurboVec and its memory compression capabilities
2Explanation of TurboVec as an open-source vector index
3Highlighting extreme compression without performance loss
4Call to action to check it out

Product placement

The video prominently features the GitHub repository for 'turbovec' and discusses its capabilities as an AI memory compression tool.

Original caption

🚨 GOOGLE JUST SHRANK 31GB OF AI MEMORY DOWN TO 4GB TurboVec is a new open-source vector index built on Google Research’s TurboQuant that delivers extreme compression without sacrificing much performance. Ships with: • 16x memory reduction (31 GB → 4 GB on 10M vectors) • 2–4 bit quantization with strong recall • Outperforms or matches FAISS on speed • Pure Rust + Python bindings, no training phase • Search-time filtering with SIMD optimizations Massive efficiency win for local and production vector search. 👉 https://github.com/RyanCodrai/turbovec Who’s running vector search on a laptop today? Drop a 🔥

More from @whitewhoadie

Call to action

Definitely check it out, boys.

On-screen text

GOOGLE JUST SHRINK 31GB OF AI MEMORY DOWN TO 4GB turbovec Google's TurboQuant for vector search A 10 million document corpus takes 31GB of RAM as float32, turbovec fits it in 4 GB - and searches it faster than FAISS. TurboVec is a Rust vector index with Python bindings, built on Google Research's TurboQuant algorithm - a data-oblivious Quantizer that matches the Shannon lower bound on distortion, with no codebook training and no separate train phase. Online ingest: Add vectors, they're indexed - no train step, no parameter tuning, no rebuilds as the corpus grows. Faster than FAISS. Hand-written NEON (ARM) and AVX-GT26W (x86) kernels beat FAISS index PqFastIscan by 12-20% on ARM and match-or-beat it on x86. Filter at search time. Pass an ID allowlist (or a slot bitmask) to search() and the kernel honours it directly. You get to filter by ID, or by IDs from the provided set - no own detecting, no reset hit on selective filters. Pure Rust, no-aged service data leaving your machine or VPC. Pair with any open-source model for a fully air-gapped RAG stack. Building RAG where privacy, memory, or latency matters? You're in the right place. Python pip install turbovec from turbovec import TurboQuantIndex index = TurboQuantIndex(file_path='data.json', num_vectors=10000000, dim=1024, bit_width=4) index.add_vectors(vectors) index.add_vectors_to_index(vectors) https://github.com/RyanCodrai/turbovec

Transcript

What's up guys? So Google just found a way to shrink 31 gigabytes of AI memory down to 4 gigs, bro. What? So turbovec is a new open source vector index built on Google Research's TurboQuant. So that delivers extreme compression without sacrificing much performance. So very nice. Definitely check it out, boys.