How much GPU memory does MNLP_M2_document_encoder need?

About 1.7 GB: 0.44 GB of weights and the runtime's own memory.

What is the cheapest GPU to run MNLP_M2_document_encoder?

Right now one GTX 1650 at $0.06 an hour; RTX 5090 at $0.67 an hour is the best value for its speed. Prices are live offers from GPU clouds, checked every 15 minutes.

Can MNLP_M2_document_encoder run on an RTX 4090?

Yes: it needs about 1.7 GB and an RTX 4090 has 24 GB.

Models / MNLP_M2_document_encoder

cristiano-sartori/MNLP_M2_document_encoder

Embeddings · served with ONNX Runtime · ↓ 153 downloads a month · View on Hugging Face

cristiano-sartori/MNLP_M2_document_encoder needs about 1.7 GB of GPU memory. It runs on one GTX 1650 from $0.06/h right now; best value: RTX 5090 at $0.67/h.

Weights: 440 MB
GPU memory needed: about 1.7 GB
Memory · disk: 8 GB · 30 GB
Licence: apache-2.0

Versions

Smaller versions (8-bit, 4-bit) need less GPU memory and answer a little less precisely.

full precisiononnx/model.onnx440 MB · 1.7 GB GPU full precisiononnx/model_O1.onnx440 MB · 1.7 GB GPU full precisiononnx/model_O2.onnx440 MB · 1.7 GB GPU full precisiononnx/model_O3.onnx440 MB · 1.7 GB GPU full precisiononnx/model_O4.onnx220 MB · 1.3 GB GPU 8-bitonnx/model_qint8_arm64.onnx110 MB · 1.2 GB GPU 8-bitonnx/model_qint8_avx512.onnx110 MB · 1.2 GB GPU 8-bitonnx/model_qint8_avx512_vnni.onnx110 MB · 1.2 GB GPU

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_M2_document_encoder need?: About 1.7 GB: 0.44 GB of weights and the runtime's own memory.
What is the cheapest GPU to run MNLP_M2_document_encoder?: Right now one GTX 1650 at $0.06 an hour; RTX 5090 at $0.67 an hour is the best value for its speed. Prices are live offers from GPU clouds, checked every 15 minutes.
Can MNLP_M2_document_encoder run on an RTX 4090?: Yes: it needs about 1.7 GB and an RTX 4090 has 24 GB.

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