1. NVIDIA Corporation (NASDAQ:NVDA)
Latest Quarter YoY Revenue Growth: 205.6%
Number of Hedge Fund Holders: 180
NVIDIA Corporation (NASDAQ:NVDA) is a California-based company that manufactures and sells graphics processors, mobile technologies, and desktop computers.
In the third quarter, hedge fund sentiment was positive toward NVIDIA Corporation (NASDAQ:NVDA)’s stock. 180 hedge funds had investments in the stock, compared to 175 in the previous quarter. The most significant stakeholder in the company was GQG Partners, with 14.04 million company shares worth $6.1 billion.
On December 15, BofA chose NVIDIA Corporation (NASDAQ:NVDA) as one of its five top picks in semiconductors for 2024. The firm has a Buy rating on the stock with a $700 price target.
Blue Tower Asset Management commented on NVIDIA Corporation (NASDAQ:NVDA) in its third quarter, 2023 investor letter. Here is what it said:
“In addition to the use of larger datasets, the training speed of AI models has increased dramatically. NVIDIA Corporation (NASDAQ:NVDA)’s stock almost tripled in the first 3 quarters of this year with a 197% gain, and a large reason for this is the huge role they have played in recent AI improvements. Nvidia’s single GPU AI training speed performance has increased by a dramatic 1000x in 10 years with only 2.5x coming from Moore’s Law3 driven increases in chip density. Besides better chip manufacturing, there were three other improvement factors at play: simplifications in number representation for the weights of the neural networks, more complex mathematical instructions for reducing the computational overhead involved in mathematical calculations, and increased neuron sparsity (in neural networks, some neurons are useless and can be pruned from the network without reducing performance significantly). In addition to these single GPU improvements, Nvidia also made improvements in data center scale architecture that allows groups of GPUs to work more efficiently together.
It is noteworthy that the vast majority of the improvement came from hardware architectural and software data improvements, rather than transition density. These improvements were likely the low-hanging fruit of training speed improvements as researchers will eventually converge on an ideal architecture. The simplification of going from 32-bit to 8-bit floating point numbers for measuring weights is a one-time gain that can’t be repeated again. I expect the rate of improvement to slow down over the next ten years and eventually approach the levels of Moore’s Law improvements in chip efficiency. The historical trend for computer hardware is for it to eventually be commoditized, and I believe this will eventually occur for Nvidia’s GPUs as well.”
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