How much GPU memory does embedic-large need?

About 2.8 GB: 1.12 GB of weights and the runtime's own memory.

What is the cheapest GPU to run embedic-large?

Right now one RTX 3060 at $0.08 an hour; RTX PRO 5000 at $0.46 an hour is the best value for its speed. Prices are live offers from GPU clouds, checked every 15 minutes.

Can embedic-large run on an RTX 4090?

Yes: it needs about 2.8 GB and an RTX 4090 has 24 GB.

How do I deploy embedic-large as an API?

Pick a machine on its GridPup page and press Deploy: GridPup rents the GPU, downloads the model from Hugging Face and serves it at an https address with keys and OpenAI's embeddings API, billed by the hour.

Models / embedic-large

djovak/embedic-large

Embeddings · served with vLLM · ↓ 250 downloads a month · View on Hugging Face

djovak/embedic-large needs about 2.8 GB of GPU memory. It runs on one RTX 3060 from $0.08/h right now; best value: RTX PRO 5000 at $0.46/h.

The model's publisher asks users to accept its licence (mit). You accept it when you deploy.

Weights: 1.1 GB
GPU memory needed: about 2.8 GB
Memory · disk: 8 GB · 42 GB
Architecture: XLMRobertaModel
Licence: mit

Hardware and deploy

Endpoint nameI accept the model's licence (mit) and its acceptable use policy.

$0.46/h from your balance while it runs, billed by the hour; stop it from its page. GridPup rents the machine, which downloads the model from Hugging Face (usually ready in 10 to 15 minutes), and gives you an https address, keys and a Playground.

Questions

How much GPU memory does embedic-large need?: About 2.8 GB: 1.12 GB of weights and the runtime's own memory.
What is the cheapest GPU to run embedic-large?: Right now one RTX 3060 at $0.08 an hour; RTX PRO 5000 at $0.46 an hour is the best value for its speed. Prices are live offers from GPU clouds, checked every 15 minutes.
Can embedic-large run on an RTX 4090?: Yes: it needs about 2.8 GB and an RTX 4090 has 24 GB.
How do I deploy embedic-large as an API?: Pick a machine on its GridPup page and press Deploy: GridPup rents the GPU, downloads the model from Hugging Face and serves it at an https address with keys and OpenAI's embeddings API, billed by the hour.

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