# Overview Applications Framework Converters Runtime Framework Low Level Libraries AI Engine Open Source Qualcomm GPU Backend Libraries NPU Backend Libraries CPU Backend Libraries TFLite AI Engine Direct (QNN) Neural Processing Engine (SNPE) TensorFlow Lite TensorFlow PyTorch ONNX Third party Developers can bring models from ONNX, PyTorch, TensorFlow or TFLite and run them efficiently on Qualcomm AI Hardware – HTP (NPU), GPU, CPU using Qualcomm AI SDKs. ## AI hardware - **Qualcomm Kryo™ CPU**: Best-in-class CPU with high performance and remarkable power efficiency. - **Qualcomm Adreno GPU**: Suitable to execute AI workloads with balanced power and performance. AI workloads are accelerated with OpenCL kernels. The GPU can also be used to accelerate model pre/postprocessing. - **Qualcomm Hexagon Tensor Processor (HTP)**: Also known as NPU/DSP/HMX, suitable to execute AI workloads with low-power and high-performance. For optimized performance, pretrained models need be quantized to one of the supported precisions. ## AI software AI stack contains SDKs to harness the power of AI hardware accelerators. Developers can use one of the SDKs of their choice to deploy AI workloads. Pretrained models (except for TFLite models) need to be converted to an executable format with the selected SDK before running them. TFLite models can be run directly using TFLite Delegate. TFLite TFLite models can be executed natively on Qualcomm hardware with acceleration using the following Delegates. | Delegate | Acceleration | | --- | --- | | AI Engine Direct Delegate (QNN Delegate) | CPU, GPU and HTP | | XNNPACK Delegate | CPU | | GPU Delegate | GPU | Qualcomm Neural Processing Engine SDK (SNPE) Qualcomm Neural Processing Engine (SNPE) is a software accelerated runtime for execution of deep neural networks. SNPE offers tools to convert, quantize neural networks and accelerate them on hardware accelerators including CPU, GPU, and HTP. Qualcomm AI Engine Direct (QNN) Qualcomm AI Engine Direct is a software architecture for AI/ML use cases on Qualcomm chipsets and AI acceleration cores. The architecture is designed to provide a unified API and modular and extensible per-accelerator libraries, which form a reusable basis for full stack AI solutions. It provides support for runtimes such as Qualcomm Neural Processing SDK, TFLite AI Engine Direct Delegate. Last Published: Jan 21, 2026 [Previous Topic AI-ML developer workflow](https://docs.qualcomm.com/bundle/publicresource/80-70017-15B/topics/ai-ml-developer-workflow.md) [Next Topic Compile and optimize model](https://docs.qualcomm.com/bundle/publicresource/80-70017-15B/topics/compile-and-optimize-model.md)