Jensen Huang, vd för Nvidia, blir intervjuad. (Chiang Ying-ying /AP/TT / AP)

Nvidia rusar i förhandeln på Wall Street

Nvidia slog förväntningarna för intäkter och visade stark efterfrågan nästa år. Det positiva mottagandet späddes på när det framkom att Amazon ska köpa ytterligare två miljoner Nvidia-GPU:er.

Vd Jensen Huang sa också att AI har nått en brytpunkt där antalet bolag som behöver de avancerade chippen har ökat dramatiskt, skriver CNBC. Marknaden köper budskapet – och ignorerar eventuella orosmoln kring belåning och konkurrens i sektorn.

Aktien rusar 7 procent i förhandeln på Wall Street.

 
Graphics processing unit (GPU)
Wikipedia (en)
A graphics processing unit (GPU) is a specialized electronic circuit designed for digital image processing and to accelerate computer graphics, being present either as a component on a discrete graphics card or embedded on motherboards, mobile phones, personal computers, workstations, and game consoles. GPUs are also increasingly being used for artificial intelligence (AI) processing and model training due to linear algebra acceleration, which is also used extensively in graphics processing. Although there is no single definition of the term, and it may be used to describe any video display system, in modern use a GPU includes the ability to internally perform the calculations needed for various graphics tasks, like rotating and scaling 3D images, and often the additional ability to run custom programs known as shaders. This contrasts with earlier graphics controllers known as video display controllers which had no internal calculation capabilities, or blitters, which performed only basic memory movement operations. The modern GPU emerged during the 1990s, adding the ability to perform operations like drawing lines and text without CPU help, and later adding 3D functionality. Graphics functions are generally independent and this lends these tasks to being implemented on separate calculation engines. Modern GPUs include hundreds, or thousands, of calculation units. This made them useful for non-graphic calculations involving embarrassingly parallel problems due to their parallel structure. The ability of GPUs to rapidly perform vast numbers of calculations has led to their adoption in diverse fields including artificial intelligence (AI) where they excel at handling data-intensive and computationally demanding tasks. Other non-graphical uses include the training of neural networks and cryptocurrency mining.
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