and parallel efficiency for scanning TrEMBL with 20 query sequences of CUDASW++4.0 using multiple A100 and H100 GPUs Number of GPUs 1 2 48 A100 runtime A100 speedup A100 parallel efficiency H100 runtime H100 speedup H100 parallel efficiency 34m 12s 1 100.0% 15m 5s 1 100.0% 17m 30s 1.95...
This unlocks a lot of exciting use cases, including on-device and in-browser execution, edge computing, low power and embedded applications. + +We apply this recipe to train two extremely efficient embedding models: [sentence-transformers/static-retrieval-mrl-en-v1](https://huggingface.co/...
1 : Don't do anything special with edge case Rounding, to go as fast as possible (no INF/NAN/Overflow -> MIN_INT conversion) (default, faster) BOX64_DYNAREC_SAFEFLAGS * Handling of flags on CALL/RET opcodes 0 : Treat CALL/RET as if it never needs any flags (faster but not advi...
In addition to large model training, the ONNX Runtime training team is also building new solutions for learning on the edge – training on devices that are constrained on memory and power. Getting Started We invite you to check out the links below to learn more about, a...
OpenVINO automatically optimizes the model for thebfloat16format. Thanks to this, the average latency is now16.7 seconds, a sweet 2x speedup. The pipeline above support dynamic input shapes, with no restriction on the number of images or their resolution. With Stable Diffusion,...
This is a 10x speedup and the latest version includes padding too! Since this step is only computed once, the actual speed is not important but overall reducing the number of operations and tensor creation is a good direction. Other parts come out more clearly when y...
This is a 10x speedup and the latest version includes padding too! Since this step is only computed once, the actual speed is not important but overall reducing the number of operations and tensor creation is a good direction. Other parts come out more clearly when you start...
Optimum integrated machine learning accelerators like ONNX Runtime and specialized hardware like Intel's Habana Gaudi, so users can benefit from considerable speedup in both training and inference. Besides, Optimum seamlessly integrates other Hugging Face’s tools while inheriting the same e...
Optimum integrated machine learning accelerators like ONNX Runtime and specialized hardware like Intel's Habana Gaudi, so users can benefit from considerable speedup in both training and inference. Besides, Optimum seamlessly integrates other Hugging Face’s tools while inheriting the same ease...
In addition to large model training, the ONNX Runtime training team is also building new solutions for learning on the edge – training on devices that are constrained on memory and power. Getting Started We invite you to check out the links below to learn more ab...