We are fans.
Before we go any further, our NVIDIA boilerplate: we make very few calls
on individual names on the blog but this one is special.
They are positioned to be the brains in autonomous vehicles, they will
drive virtual reality should it ever catch on, the current businesses
include gaming graphics, deep learning/artificial intelligence, and
supercharging the world's fastest supercomputers including what will be
the world's fastest at Oak Ridge next year.
Not just another pretty face.
Or food delivery app.
After hours the stock is changing hands at $38.31 up 7.70% which, if it
holds through tomorrow's regular session, beats the old highs from 2007....
Nvidia did a 4:1 stock split in 2021 so prices prior to the split should be adjusted by dividing by four. That $38.31 results in an ATH price that seems almost quaint: $9.58.
We haven't done anything with NVDA in quite a while, and don't expect we'll get to relive the glory days (#1 performer in the S&P in 2017) $25 to $130 here on the blog but the most recent earnings report was good, and if they can get the data center business up to potential the stock has some upside with a lot less air in the price than say, a year ago. If interested check the search blog box for more, we have a couple hundred posts on this one, it was a fun ride....
Mr. Huang was telling us what was coming the whole time it was coming.
But despite all that we never posted a simple explanation of what was going on. Until today.
Here's AI Multiple, January 5, 2023:
In last decade, machine learning, especially deep neural networks
have played a critical role in the emergence of commercial AI
applications. Deep neural networks were successfully implemented in
early 2010s thanks to the increased computational capacity of modern
computing hardware. AI hardware is a new generation of hardware custom
built for machine learning applications.
As the artificial intelligence and its applications become more
widespread, the race to develop cheaper and faster chips is likely to
accelerate among tech giants. Companies can either rent these hardware
on the cloud from cloud service providers like Amazon AWS’ Sagemaker
service or buy their hardware. Own hardware can result in lower costs if
utilization can be kept high. If not, companies are better off relying
on the cloud vendors.
What are AI chips?
AI chips (also called AI hardware or AI accelerator) are specially designed accelerators for artificial neural network (ANN) based applications. Most commercial ANN applications are deep learning applications.
ANN is a subfield of artificial intelligence. ANN is a machine
learning approach inspired by the human brain. It includes layers of
artificial neurons which are mathetical functions inspired by how human
neurons work. ANNs can be built as deep networks with multiple layers.
Machine learning applications using such networks is called deep learning. Deep learning has 2 main use cases:
Training: A deep ANN is fed thousands of labeled data so it can
identify patterns. Training is time consuming and intensive for
computing resources
Inference: As a result of the training process, ANN is able to make predictions based on new inputs.
Although
general purpose chips can also run ANN applications, they are not the
most effective solution for these software. There are multiple types of
AI chips as customization is necessary in different types of ANN
applications. For example, in some IoT applications where IoT devices
need to operate on a battery, AI chips would need to be physically small
and built to function efficiently with low power. This leads chip
manufacturers to make different architectural choices while designing
chips for different applications
What are AI chip’s components?
The hardware infrastructure of an AI chip consists of three parts:
computing, storage and networking. While computing or processing speed
have been developing rapidly in recent years, it seems like some more
time is needed for storage and networking performance upgrades. Hardware
giants like Intel, IBM, Nvidia are competing to improve the storage and
networking modules of the hardware infrastructure.
Why are AI chips higher performing than general purpose chips?
General purpose hardware uses arithmetic blocks for basic in-memory
calculations. The serial processing does not give sufficient performance
for deep learning techniques.
Neural networks need many parallel/simple arithmetic operations
Powerful general purpose chips can not support a high number of simple, simultaneous operations
AI optimized HW includes numerous less powerful chips which enables parallel processing
The AI accelerators bring the following advantages over using general purpose hardware:....
....MUCH MORE, he does an excellent job of making things intelligible.
