owlvit-base-patch32
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve google/owlvit-base-patch32New to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
owlvit-base-patch32 is an open-weight object detection model from google, published on Hugging Face in July 2022. It has 150M parameters and is released under the apache-2.0 license.
HardwareLink to this section
At full FP16 precision the weights need about 1 GB of memory; 8-bit quantization brings that to roughly 1 GB. That is small enough for most consumer GPUs, Apple Silicon Macs, and many CPUs.
PopularityLink to this section
owlvit-base-patch32 had 140,507 downloads on Hugging Face in the last month and has 159 likes.
Run it locallyLink to this section
The weights are on Hugging Face and load with Hugging Face Transformers. Follow the model card for the exact setup and license terms.
Frequently asked
Quick answers to common questions
How much VRAM does owlvit-base-patch32 need?
owlvit-base-patch32 with 0.15B parameters needs approximately 1 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is owlvit-base-patch32 better than other google models?
owlvit-base-patch32 has 0.15B parameters with 16 context - a strong choice for object-detection.
What license is owlvit-base-patch32 under?
owlvit-base-patch32 is released under the apache-2.0 license, making it suitable for most commercial and personal projects.
What hardware runs owlvit-base-patch32 well?
With 0.15B parameters, owlvit-base-patch32 requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for owlvit-base-patch32?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~1 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
What models compete with owlvit-base-patch32?
owlvit-base-patch32 competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec