jina-embeddings-v5-text-small

This architecture allocates no KV cache. Encoder and embedding models process their input in one pass rather than generating token by token, so there is nothing to carry forward between steps and memory does not grow with context. Its footprint is the weights plus a working buffer, and that is the whole story.

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 0.31 GiB. The real file is 0.37 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

How much VRAM does jina-embeddings-v5-text-small need?

Q4_K_M is exactly 396,705,152 bytes (0.37 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.

Which quantization of jina-embeddings-v5-text-small should I use?

Q4_K_M is the usual default. Pick the largest quantization that fits your card at the context you actually need — the table above gives exact sizes for every one published.