NLP |
deepseek |
Link |
QDQ: QDQ Model with 4-bit Weights & 16-bit Activations
Qualcomm NPU: PTQ + AOT Compilation for Qualcomm NPUs using QNN EP
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llama2 |
Link |
CPU: with ONNX Runtime optimizations for optimized FP32 ONNX model
CPU: with ONNX Runtime optimizations for optimized INT8 ONNX model
CPU: with ONNX Runtime optimizations for optimized INT4 ONNX model
GPU: with ONNX Runtime optimizations for optimized FP16 ONNX model
GPU: with ONNX Runtime optimizations for optimized INT4 ONNX model
GPU: with QLoRA for model fine tune and ONNX Runtime optimizations for optimized ONNX model
AzureML compute: with AzureML compute to fine tune and optimize for your local GPUs
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llama3 |
Link |
QDQ: QDQ Model with 4-bit Weights & 16-bit Activations
Qualcomm NPU: PTQ + AOT Compilation for Qualcomm NPUs using QNN EP
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mistral |
Link |
CPU: with Optimum conversion and ONNX Runtime optimizations and Intel® Neural Compressor static quantization for optimized INT8 ONNX model
GPU: with ONNX Runtime optimizations for optimized FP16 ONNX model
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open llama |
Link |
GPU: with Optimum conversion and merging and ONNX Runtime optimizations for optimized ONNX model
GPU: with SparseGPT and TorchTRT conversion for an optimized PyTorch model with sparsity
AzureML compute: with Optimum conversion and merging and ONNX Runtime optimizations in AzureML
CPU: with Optimum conversion and merging and ONNX Runtime optimizations and Intel® Neural Compressor 4-bits weight-only quantization for optimized INT4 ONNX model
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phi2 |
Link |
CPU: with ONNX Runtime optimizations fp32/int4
GPU with ONNX Runtime optimizations fp16/int4, with PyTorch QLoRA for model fine tune
GPU with SliceGPT for an optimized PyTorch model with sparsity
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phi3.5 |
Link |
QDQ: QDQ Model with 4-bit Weights & 16-bit Activations
Qualcomm NPU: PTQ + AOT Compilation for Qualcomm NPUs using QNN EP
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qwen2.5 |
Link |
QDQ: QDQ Model with 4-bit Weights & 16-bit Activations
Qualcomm NPU: PTQ + AOT Compilation for Qualcomm NPUs using QNN EP
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falcon |
Link |
GPU: with ONNX Runtime optimizations for optimized FP16 ONNX model
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red pajama |
Link |
CPU: with Optimum conversion and merging and ONNX Runtime optimizations for a single optimized ONNX model
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bert |
Link |
CPU: with ONNX Runtime optimizations and quantization for optimized INT8 ONNX model
CPU: with ONNX Runtime optimizations and Intel® Neural Compressor quantization for optimized INT8 ONNX model
CPU: with PyTorch QAT Customized Training Loop and ONNX Runtime optimizations for optimized ONNX INT8 model
GPU: with ONNX Runtime optimizations for CUDA EP
GPU: with ONNX Runtime optimizations for TRT EP
NPU: with ONNX Runtime optimizations for QNN EP
QDQ: with ONNX Runtime optimizations and INT8 quantization encoded in QDQ format
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deberta |
Link |
GPU: Optimize Azureml Registry Model with ONNX Runtime optimizations and quantization
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gptj |
Link |
CPU: with Intel® Neural Compressor static/dynamic quantization for INT8 ONNX model
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bge |
Link |
NPU: with ONNX Runtime optimizations for QNN EP
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Audio |
whisper |
Link |
CPU: with ONNX Runtime optimizations for all-in-one ONNX model in FP32
CPU: with ONNX Runtime optimizations for all-in-one ONNX model in INT8
CPU: with ONNX Runtime optimizations and Intel® Neural Compressor Dynamic Quantization for all-in-one ONNX model in INT8
GPU: with ONNX Runtime optimizations for all-in-one ONNX model in FP32
GPU: with ONNX Runtime optimizations for all-in-one ONNX model in FP16
GPU: with ONNX Runtime optimizations for all-in-one ONNX model in INT8
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audio spectrogram
transformer |
Link |
CPU: with ONNX Runtime optimizations and quantization for optimized INT8 ONNX model
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Vision |
stable diffusion
stable diffusion XL |
Link |
GPU: with ONNX Runtime optimization for DirectML EP
GPU: with ONNX Runtime optimization for CUDA EP
Intel CPU: with OpenVINO toolkit
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squeezenet |
Link |
GPU: with ONNX Runtime optimizations with DirectML EP
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mobilenet |
Link |
Qualcomm NPU: with ONNX Runtime static QDQ quantization for ONNX Runtime QNN EP
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clip |
Link |
Qualcomm NPU: with ONNX Runtime static QDQ quantization for ONNX Runtime QNN EP
QDQ: with ONNX Runtime static Quantization for ONNX INT8 model with QDQ format
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resnet |
Link |
CPU: with ONNX Runtime static/dynamic Quantization for ONNX INT8 model
QDQ: with ONNX Runtime static Quantization for ONNX INT8 model with QDQ format
CPU: with PyTorch QAT Default Training Loop and ONNX Runtime optimizations for ONNX INT8 model
CPU: with PyTorch QAT Lightning Module and ONNX Runtime optimizations for ONNX INT8 model
AMD DPU: with AMD Vitis-AI Quantization
Intel GPU: with ONNX Runtime optimizations with multiple EPs
Qualcomm NPU: with ONNX Runtime static QDQ quantization for ONNX Runtime QNN EP
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VGG |
Link |
Qualcomm NPU: with SNPE toolkit
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inception |
Link |
Qualcomm NPU: with SNPE toolkit
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super resolution |
Link |
CPU: with ONNX Runtime pre/post processing integration for a single ONNX model
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Vision Transformer |
Link |
Qualcomm NPU: with ONNX Runtime static QDQ quantization for ONNX Runtime QNN EP
QDQ: with ONNX Runtime static Quantization for ONNX INT8 model with QDQ format
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Table Transformer Detection |
Link |
Qualcomm NPU: with ONNX Runtime static QDQ quantization for ONNX Runtime QNN EP
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