[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]

![rohanpaul_ai Avatar](https://lunarcrush.com/gi/w:24/cr:twitter::2588345408.png) Rohan Paul [@rohanpaul_ai](/creator/twitter/rohanpaul_ai) on x 74.1K followers
Created: 2025-07-18 19:50:44 UTC

🧵 2/n.  Why constant space matters

Every document now carries the same vector count, so the index grows linearly with corpus size rather than document length.

Fixed length lets the database pack vectors into cache‑friendly blocks, which improves paging and SIMD throughput, and it roughly halves index size compared with unpooled ColBERT. 

This approach makes it:

- Easier to manage and scale in a vector database: All documents have uniform storage sizes, simplifying retrieval logic.

- More efficient for query-time processing: Avoids the overhead of variable-length comparisons, leading to better cache locality and SIMD optimizations.

- Compatible with real-world applications: Allows batch processing of documents without worrying about inconsistent representation sizes.

![](https://pbs.twimg.com/media/GwKg9K6acAA_gVn.png)

XXX engagements

![Engagements Line Chart](https://lunarcrush.com/gi/w:600/p:tweet::1946296598885233143/c:line.svg)

**Related Topics**
[blocks](/topic/blocks)

[Post Link](https://x.com/rohanpaul_ai/status/1946296598885233143)