voyage-large-2 produces 1536-dimensional text embeddings. That means every text you embed is converted into a vector of length 1536 (typically floats), and your downstream storage and search stack must be configured to accept vectors with that exact dimension. This is not just a detail; it’s a schema constraint. If you create a collection in your vector database with dimension 1024 and then try to insert 1536-d vectors, inserts will fail. The Zilliz model page lists the dimension explicitly as 1536, along with the model’s max input tokens and pricing information.
For developers, “dimension” matters in three concrete places. First, database schema: in Milvus or Zilliz Cloud, your collection’s vector field must be defined as a float vector with dimension 1536. Second, indexing and memory: larger vectors consume more memory and can increase index size, which affects cost and query latency. The right index type and parameters depend on scale and latency targets, but dimension always influences resource planning (RAM footprint, cache behavior, and CPU cost per distance calculation). Third, interoperability: you must embed both documents and queries with the same model (and therefore the same dimension). Mixing dimensions or mixing models in the same collection generally breaks similarity search because vectors won’t be comparable in a meaningful way.
You’ll often see dimension show up in operational decisions like migration and A/B testing. If you want to compare voyage-large-2 against another embedding strategy, the safest approach is to create a separate collection (or separate vector field) and index them independently. That way you can run the same query against two collections, compare relevance and latency, and then decide which one to keep. If you later change models, you typically need to re-embed your corpus and rebuild indexes because the geometry of the vector space changes—even if the dimension happens to stay the same. The practical takeaway: treat “1536” as a contract for your schema and infrastructure, and build your ingestion jobs so they always validate vector length before inserting into Milvus or Zilliz Cloud, especially when you run batch pipelines at scale.
For more information, click here: https://zilliz.com/ai-models/voyage-large-2