gte-small API: Pricing & Specs
gte-small is an open-weight embedding model that turns text into vectors for search and retrieval.
What is gte-small?
gte-small is an open-weight embedding model that turns text into vectors for search and retrieval, published by thenlper with open weights at thenlper/gte-small. LLM API does not route this model today, so there is no endpoint or code sample for it here. What this page does give you is the self-hosting picture: published weight sizes, the VRAM each quantisation needs and the commands to serve it yourself. Every figure is read from the public repository and refreshed weekly. At import it had 1,091,694 downloads on Hugging Face in the previous 30 days, under the mit licence.
Can you self-host gte-small?
No. We found no public weights for gte-small on Hugging Face, so there is nothing to download, quantize or serve on your own GPUs: no VRAM budget, no vLLM or GGUF build to plan for. The only way to run it is over an API.
Not on LLM.API yet
We do not route this model through our API at the moment. Browse the models you can call today — most workloads have a close match already live.
Not available on LLM API yet
We do not route this model through our API at the moment, so there is no endpoint or code snippet for it yet. Browse the models you can call today — most workloads have a close match already live.
Browse available modelsWhen to Use — When NOT to Use
Use it if...
- You need vectors for semantic search, clustering or RAG retrieval (gte-small)
- You want to keep documents inside your own network while indexing them
- The weights are published openly, so you can pin one checkpoint and keep vectors reproducible
- You are comparing self-hosting cost against a managed embedding endpoint
Avoid if...
- LLM API does not route this model today, so there is no endpoint here to call
- You want zero operations — serving, scaling and upgrades are yours to run
- You need text generation — this model returns vectors or scores, not answers
- You already have an index built with a different model: vectors are not interchangeable
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