CUDA almost blew a hole in Nvidia’s finances, according to chief executive Jensen Huang. Huang told the Lex Fridman podcast that the 2006 push to make GeForce GPUs programmable was a bet that could ...

Nvidia's strategic tie-up with fellow chipmaker Marvell Technology is yet another reason to stick with the AI giant's sluggish stock. On Tuesday morning, the companies announced a partnership to ...

Intel is announcing its new Arc Pro B70 “Big Battlemage” desktop GPU with 32GB of VRAM and up to 32 Xe2 cores. It costs $949 for an Intel reference design, while partner designed cards will vary in ...

When Nvidia first showed off its Compute Unified Device Architecture (CUDA) parallel computing platform in 2006, it was a multibillion-dollar bet that failed to turn a profit for a decade. Today, it ...

In this tutorial, we explore how to use NVIDIA Warp to build high-performance GPU and CPU simulations directly from Python. We begin by setting up a Colab-compatible environment and initializing Warp ...

WASHINGTON, DC - APRIL 30: U.S. President Donald Trump (L) listens as Nvidia CEO Jensen Huang speaks in the Cross Hall of the White House during an event on "Investing in America" on April 30, 2025, ...

NVIDIA's new cuda.compute library topped GPU MODE benchmarks, delivering CUDA C++ performance through pure Python with 2-4x speedups over custom kernels. NVIDIA's CCCL team just demonstrated that ...

Graphics Cards Best graphics cards in 2026: I've tested pretty much every AMD and Nvidia GPU of the past 20 years and these are today's top cards Graphics Cards Nvidia RTX 5060 Ti 8 GB review (Palit ...

Rumors of the demise of Intel's GPU business have been greatly exaggerated. Even before the launch of the first Alchemist-based Intel Arc GPUs, naysayers were insisting that Intel would kill the brand ...

Analysts say Intel’s success will hinge less on hardware and more on overcoming entrenched software lock-in and buyer inertia. Intel is making a new push into GPUs, this time with a focus on data ...

GPU memory (VRAM) is the critical limiting factor that determines which AI models you can run, not GPU performance. Total VRAM requirements are typically 1.2-1.5x the model size due to weights, KV ...