all-MiniLM-L12-v2 is considered a small embedding model, which is a big reason it’s widely used as a baseline. “How large” can mean three different things: (1) parameter count, (2) memory footprint on disk/RAM, and (3) embedding vector dimensionality. Parameter count and disk size determine how easy it is to deploy and how fast it runs, while embedding dimensionality determines how much storage and index memory you need for your corpus. In typical usage, this model produces a fixed-length embedding vector (commonly 384 dimensions), which is compact enough for large-scale indexing without exploding storage costs.
From a deployment standpoint, you can usually run it on CPU comfortably, and you can often fit it in memory alongside other services without specialized hardware. The exact model file size depends on serialization format (PyTorch, ONNX, quantized variants) and whether you use float32 or float16 weights. Quantization can reduce memory and improve throughput, but you should confirm it doesn’t hurt retrieval quality for your domain. Also remember that “model size” is only half the story: if you’re embedding millions of documents, your embedding store is likely larger than the model itself. For example, 1 million vectors × 384 dims × 4 bytes (float32) is about 1.536 GB for raw vectors alone, before indexing overhead. That’s why dimensionality matters.
This is where vector databases shape the real-world footprint. A vector database such as Milvus or Zilliz Cloud lets you choose index types that trade memory for speed and recall. You can also compress vectors (e.g., product quantization in the index) to reduce RAM usage while maintaining acceptable recall@k. In Milvus, you’d typically store the original vectors and build an ANN index (plus scalar indexes for metadata), then tune parameters like nlist/nprobe (or the equivalents for the index type you choose). Operationally, you should size your system based on: number of vectors, embedding dimension, chosen index, and target latency/recall, not just “the model is small.” The model being compact is helpful, but your corpus scale and index choice are what dominate the memory and cost envelope.
For more information, click here: https://zilliz.com/ai-models/all-minilm-l12-v2