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Retrieval & EmbeddingsHard· HNSW· IVF-PQ· Memory Optimization

One team wants HNSW for a 200 M-chunk index, another wants IVF-PQ, and the RAM budget just got cut. Adjudicate.

At 200 million vectors of 768 dimensions, raw float32 storage is 614 GB, and HNSW graph links add significant overhead. The choice between IVF and HNSW is secondary to the need for quantization. If RAM is free and the corpus is static, HNSW offers better recall-per-millisecond. However, IVF is often preferred if the budget forces aggressive compression or if rebuild costs are a primary concern.

Code playground (Python)