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…Maximize AI PC inference capabilities from large language models (LLM) to image generation with Intel® oneAPI Deep Neural Network Library (oneDNN) and PyTorch optimizations for Intel® Core™ Ultra processors (series 2) and…
…Intel® Software Development Tools Quickly Deploy Fast Responsive AI Significant updates were made to the rich and optimized solutions for PyTorch* and TensorFlow* users and the underlying tooling and libraries: PyTorch* Optimizations…
…Intel announced Intel GPU support in the recent PyTorch* 2.4 release . Developers can now evaluate the latest Intel-supported Pytorch releases via notebooks in the Learning catalog. Llama 3.2, the…
…Read the Introductory TensorRT Blog Learn how to apply TensorRT optimizations and deploy a PyTorch model to GPUs. Watch On-Demand TensorRT Sessions From GTC Learn more about TensorRT and its features…
The smile-deep module provides idiomatic Java API for deep learning on the JVM while still reaching CPU, CUDA, and MPS backends by wrapping the PyTorch / LibTorch C++ runtime. It also provides tiktoken BPE tokenizer, LLa…
Show HN: We built an LLM inference engine in pure Python – no PyTorch, no Triton
…AI and HPC enhancements AI customers will see numerous enhancements for XGBoost and scikit-learn performance, plus upgrades to Intel® Optimization for Tensorflow and PyTorch. PyTorch provides improved INT8 optimizations, while the…
…Your PyTorch solutions are now even further enhanced with OpenVINO. You’ve got more options and you no longer need to convert to ONNX for deployment. Developers can now use their API…
…Introduced OpenVINOQuantizer class inherited from PyTorch 2 Quantizer for more accurate and efficient quantized PyTorch models for deployments with OpenVINO. Added support for nncf.quantize() method as the initialization step for Quantization…
…Out of all these frameworks, PyTorch and TensorFlow have become the most used due to their vast feature sets, extensive ecosystems and community support. While these frameworks dominate the AI landscape, Just…
…PyTorch Framework Support PyTorch version 2.6 is now supported. Common translators have been implemented to unify decompositions for operations of multiple frameworks (PyTorch, TensorFlow, ONNX, JAX) and to support complex tensors…
…NumPy with AOCL SciPy with AOCL PyTorch with AOCL NumExpr with AOCL SciPy Sparse Extension with AOCL Highlights of release 5.3 on AMD Zen CPU Architecture Enhanced PyTorch performance with AOCL…
