Abstract: SSD-resident approximate nearest-neighbor search is essential when vector collections exceed DRAM capacity. The challenge is to reduce SSD reads and hide I/O latency through concurrent reads and overlap with computation. However, for graph-based search, progressive candidate discovery limits advance I/O planning while IVF search reads inverted lists in full, incurring unnecessary SSD reads. In this paper, we present RaBitQ-SSD, an extension of IVF-RaBitQ for SSD-resident vector search. Specifically, we propose Split-RaBitQ, which retains a configurable prefix of each binary code in DRAM, allowing in-memory code storage below one bit per dimension. Using this partial representation, it provides an unbiased distance estimator with a probabilistic error bound. Once the in-memory coarse quantizer identifies candidate lists, this bound supports pruning individual candidates within them, enabling finer-grained SSD access. We also design an asynchronous search pipeline that coordinates candidate pruning with SSD read scheduling to reduce unnecessary reads while overlapping I/O with computation. On datasets ranging from $5$ million to $1$ billion vectors, RaBitQ-SSD delivers up to $1.74\times$ the throughput of graph-based baselines at $90\%$ recall, while reducing SSD page reads by up to $3.8\times$. Its on-SSD indexes are up to $7.0\times$ smaller and $10.0\times$ faster to build than DiskANN. We also build and search an index of $10$ billion vectors on a single machine with one SSD, achieving over $3{,}000$ queries per second at $90\%$ recall.