Embeddings · served with Text Embeddings Inference · ↓ 145 downloads a month · View on Hugging Face
julpiro/MNLP_M3_document_encoder needs about 1.8 GB of GPU memory. It runs on one GTX 1660 SUPER from $0.07/h right now; best value: RTX 5090 at $0.70/h.
Weights
220 MB
GPU memory needed
about 1.8 GB
Model allows
512 tokens
Memory · disk
8 GB · 21 GB
Architecture
BertModel
Licence
mit
Hardware and deploy
Deploying ONNX models from the catalog is coming soon. Today, download the files from Hugging Face and upload them in Inference.
Questions
How much GPU memory does MNLP_M3_document_encoder need?
About 1.8 GB: 0.22 GB of weights and the runtime's own memory.
What is the cheapest GPU to run MNLP_M3_document_encoder?
Right now one GTX 1660 SUPER at $0.07 an hour; RTX 5090 at $0.70 an hour is the best value for its speed. Prices are live offers from GPU clouds, checked every 15 minutes.
Can MNLP_M3_document_encoder run on an RTX 4090?
Yes: it needs about 1.8 GB and an RTX 4090 has 24 GB.
Prices are per hour for the whole machine and its disk, right now. Speed is Vast's DLPerf score of the machine, relative to the cheapest. Best value: the most speed per dollar. Only machines with a Vast reliability above 95% that download at 400 Mbit/s or more are offered: the machine fetches the model itself.