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1. Introduction: Anatomy of the BottleneckContinue reading on Medium »
NVIDIA’s dominance in AI hardware is a software story as much as a chip story. Comparing CUDA, AMD’s ROCm, and the slow, still-unfinished emergence of SYCL and WebGPU as genuinely...
ZLUDA as the open-source project working on CUDA for non-NVIDIA GPUs like AMD Radeon graphics cards now has implemented support for some of NVIDIA's core cuFFT APIs...
Learn how to leverage NVIDIA’s cuDNN Frontend Graph API to build custom kernel fusions, autotuning engine configurations, FP8-style epilogues, scaled dot-product attention, dynamic...
QCentroid has integrated NVIDIA CUDA-Q into its QuantumOps platform, creating an enterprise hybrid application design framework. This new system automates the placement of quantum...
Insider Brief PRESS RELEASE — Quantum Machines, a leading provider of advanced quantum control solutions, today became the first control company to run an end-to-end NVIDIA CUDA-Q ...
NVIDIA has launched CUDA-Q Logical, an extension to its open-source accelerated quantum computing platform, aimed at accelerating fault-tolerant quantum computing by unifying the d...
DeepSeek опубликовала средства для программирования китайских ускорителей искусственного интеллекта, разработанные совместно с Huawei. Два крупных китайских игрока совместными усил...
Traditionally, production inference systems rely on generic kernels to handle diverse...
At IEEE Quantum Week 2026, IQM Quantum Computers announced the adoption of NVIDIA CUDA-Q Logical within their Halocene quantum error correction product line. This integration allow...
Learn how DSpark speculative decoding can improve local LLM generation speed using the same GPU, with Qwen3-8B, llama.cpp, and CUDA.
Learn how DSpark speculative decoding can improve local LLM generation speed using the same GPU, with Qwen3-8B, llama.cpp, and CUDA.
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