Month: November 2024

NVIDIA’s vision of accelerated quantum supercomputers integrates quantum hardware and AI supercomputing to turn today’s quantum processors into tomorrow’s…

NVIDIA’s vision of accelerated quantum supercomputers integrates quantum hardware and AI supercomputing to turn today’s quantum processors into tomorrow’s useful quantum computing devices. At Supercomputing 2024 (SC24), NVIDIA announced a wave of projects with partners that are driving the quantum ecosystem through those challenges standing between today’s technologies and this accelerated…

Source

Everything that is manufactured is first simulated with advanced physics solvers. Real-time digital twins (RTDTs) are the cutting edge of computer-aided…

Everything that is manufactured is first simulated with advanced physics solvers. Real-time digital twins (RTDTs) are the cutting edge of computer-aided engineering (CAE) simulation, because they enable immediate feedback in the engineering design loop. They empower engineers to innovate freely and rapidly explore new designs by experiencing in real time the effects of any change in the simulation.

Source

AI has proven to be a force multiplier, helping to create a future where scientists can design entirely new materials, while engineers seamlessly transform…

AI has proven to be a force multiplier, helping to create a future where scientists can design entirely new materials, while engineers seamlessly transform these designs into production plans—all without ever setting foot in a lab. As AI continues to redefine the boundaries of innovation, this once elusive vision is now more within reach. Recognizing this paradigm shift…

Source

Quantum dynamics describes how complex quantum systems evolve in time and interact with their surroundings. Simulating quantum dynamics is extremely difficult…

Quantum dynamics describes how complex quantum systems evolve in time and interact with their surroundings. Simulating quantum dynamics is extremely difficult yet critical for understanding and predicting the fundamental properties of materials. This is of particular importance in the development of quantum processing units (QPUs), where quantum dynamics simulations enable QPU developers to…

Source

nvmath-python (Beta) is an open-source Python library, providing Python programmers with access to high-performance mathematical operations from NVIDIA CUDA-X…

nvmath-python (Beta) is an open-source Python library, providing Python programmers with access to high-performance mathematical operations from NVIDIA CUDA-X math libraries. nvmath-python provides both low-level bindings to the underlying libraries and higher-level Pythonic abstractions. It is interoperable with existing Python packages, such as PyTorch and CuPy. In this post, I show how to…

Source

Python is the most common programming language for data science, machine learning, and numerical computing. It continues to grow in popularity among scientists…

Python is the most common programming language for data science, machine learning, and numerical computing. It continues to grow in popularity among scientists and researchers. In the Python ecosystem, NumPy is the foundational Python library for performing array-based numerical computations. NumPy’s standard implementation operates on a single CPU core, with only a limited set of operations…

Source

The demand for ready-to-deploy high-performance inference is growing as generative AI reshapes industries. NVIDIA NIM provides production-ready microservice…

The demand for ready-to-deploy high-performance inference is growing as generative AI reshapes industries. NVIDIA NIM provides production-ready microservice containers for AI model inference, constantly improving enterprise-grade generative AI performance. With the upcoming NIM version 1.4 scheduled for release in early December, request performance is improved by up to 2.4x out-of-the-box with…

Source

In this blog post, we take a closer look at chunked prefill, a feature of NVIDIA TensorRT-LLM that increases GPU utilization and simplifies the deployment…

In this blog post, we take a closer look at chunked prefill, a feature of NVIDIA TensorRT-LLM that increases GPU utilization and simplifies the deployment experience for developers. This builds on our previous post discussing how advanced KV cache optimization features in TensorRT-LLM improve performance up to 5x in use cases that require system prefills. When a user submits a request to…

Source