No, cant use Tesla K20xm with 6GB VRAM for modern computation as it has Compute Capability parameter lower than required 7.0. Here you have table of my findings about libraries/frameworks, required hardware and its purpose. I started with DeepStack, where I was able to run API server for object detection, Frigate has support for it. Later on, with TensorRT on NVIDIA GPU I can run Yolov7x-640 model also for object detection, Frigate works well with it. With Google Coral TPU USB module we can run SSD MobileNet or EfficientDet models with great power efficency for good price. Ollama with moondream
What is OpenVINO? “Open-source software toolkit for optimizing and deploying deep learning models.” It is developed by Intel since 2018. It supports LLM, computer vision and generative AI. It runs on Windows, Linux and MacOS. As for Ubuntu, it is recommended to run on 22.04 LTS and higher. It utilizes OpenCL drivers. In theory, libraries using OpenCL (such as OpenVINO) should be cross platform contrary to vendor-locked similar solutions like CUDA. In theory OpenVINO should then work on both Intel and AMD hardware. Internet says that is works, but as for now I need to order some additional hardware to
These are two majors which allow to run object detection models. Google Coral TPU is a physical module which can be in a form of USB stick. TensorRT is a feature of GPU runtime. Both allows to run detection models on them. Coral TPU: And TensorRT: Compute Capabilities requirements CC 5.0 is required to run DeepStack and TensorRT, but 7.0 to run Ollama moondream:1.8b. Even having GPU with CC 5.0 which is minimum required to run for instance TensorRT might be not enough due to some minor differences in implementation. It is better to run on GPU with higher CC.