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

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