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clip-vit-base-patch32: GPU requirements and price to run | GridPup

Models / clip-vit-base-patch32

Xenova/clip-vit-base-patch32

Zero-shot image classification · served with ONNX Runtime · ↓ 208.4K downloads a month · View on Hugging Face

Weights: 610 MB
GPU memory needed: about 1.9 GB
Memory · disk: 8 GB · 30 GB
Licence: not stated

Versions

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

full precisiononnx/model.onnx610 MB · 1.9 GB GPU 4-bit (bnb)onnx/model_bnb4.onnx180 MB · 1.3 GB GPU float16onnx/model_fp16.onnx300 MB · 1.5 GB GPU 4-bitonnx/model_q4.onnx190 MB · 1.3 GB GPU 4-bit, float16onnx/model_q4f16.onnx130 MB · 1.2 GB GPU 8-bitonnx/model_quantized.onnx150 MB · 1.2 GB GPU 8-bitonnx/model_uint8.onnx150 MB · 1.2 GB GPU full precisiononnx/text_model.onnx250 MB · 1.4 GB GPU 4-bit (bnb)onnx/text_model_bnb4.onnx120 MB · 1.2 GB GPU float16onnx/text_model_fp16.onnx130 MB · 1.2 GB GPU 8-bitonnx/text_model_int8.onnx60 MB · 1.1 GB GPU 4-bitonnx/text_model_q4.onnx130 MB · 1.2 GB GPU 4-bit, float16onnx/text_model_q4f16.onnx70 MB · 1.1 GB GPU 8-bitonnx/text_model_quantized.onnx60 MB · 1.1 GB GPU 8-bitonnx/text_model_uint8.onnx60 MB · 1.1 GB GPU full precisiononnx/vision_model.onnx350 MB · 1.5 GB GPU 4-bit (bnb)onnx/vision_model_bnb4.onnx60 MB · 1.1 GB GPU float16onnx/vision_model_fp16.onnx180 MB · 1.3 GB GPU 8-bitonnx/vision_model_int8.onnx90 MB · 1.1 GB GPU 4-bitonnx/vision_model_q4.onnx60 MB · 1.1 GB GPU 4-bit, float16onnx/vision_model_q4f16.onnx50 MB · 1.1 GB GPU 8-bitonnx/vision_model_quantized.onnx90 MB · 1.1 GB GPU 8-bitonnx/vision_model_uint8.onnx90 MB · 1.1 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.

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.