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TRANSCRIPT
Introducing VectordashVectordash is a cloud GPU platform that lets anyone rent out computational power.
1. The Bitcoin Network
2. Introducing Vectordash
3. Instance Pricing
4. Artificial Intelligence on Vectordash
5. References
Miners across the world earn Bitcoin by solving the Hashcash proof of work.
The world’s most powerful computing network
In less than a decade, Bitcoin miners have assembled the world’s most powerful computing network, with a hash rate exceeding 16 exahashes per second and consuming 32 TWh of electricity annually — about as much as Denmark. Bitcoin’s mining power is spent on useless computation, wasting both electricity and computational power.
Bitcoin Network Hash Rate
What if all this computational power was spent on something useful instead?
The world’s most affordable cloud GPU provider.
Backed by a network of powerful Nvidia GPUs.
Get paid to provide GPU computePowerful GPU instances for 10x cheaper
Rent out your GPU on Vectordash and earn 1.5x
more than what you would earn mining the most
profitable cryptocurrency. GPU owners can become
a host simply by running the Vectordash desktop
client. They just have to select an availability end
date denoting how long they plan on hosting for
and then their computer is listed as available for
anyone to rent out. Our desktop client also allows
for auto-switching, so while your machine is not
being utilized on Vectordash, you can still mine
cryptocurrencies allowing you to maximize your
GPU’s earnings.
Vectordash’s GPU instances cost 10x less than their
counterparts from cloud providers such as Amazon
Web Services, Google Cloud, and Microsoft Azure.
Our distributed infrastructure allows for unused
compute anywhere to be utilized by anyone who
requests it. Our aim is to become the world’s
largest cloud provider without owning any
hardware simply by providing a thin layer of
infrastructure over existing computers. This allows
us to offer high performance instances that are an
order of magnitude more affordable than current
cloud providers.
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VectordashV
Ubuntu 16.04 deep learning image
There’s no need to spend hours installing
drivers and libraries. Instances come with
an Ubuntu 16.04 image preloaded with
CUDA, cuDNN, and popular machine
learning libraries such as TensorFlow,
PyTorch, Keras, and Caffe.
We make AI simple
Once you’ve selected an instance type,
Vectordash takes care of the rest. We’ll
give you an IP address and SSH key to
login and begin development instantly.
Simple. Powerful. Affordable.
$ ssh [email protected] -i key
Welcome to Ubuntu 16.04.3
$ python train.py
Step 1 / 300,000, train_loss = 4.504
Step 2 / 300,000, train_loss = 4.503
Step 3 / 300,000, train_loss = 4.499
Vectordash is the simplest way to get startedwith machine learning, artificial intelligence, and data science.
Vectordash GPU instances are... 8.4x cheaper than Google Cloud
The world’s most affordable cloud provider.By far.
Consumer-grade GPUs are much more powerful than
their datacenter counterparts. For instance, an Nvidia
1080 ti can train a neural network 5.5x faster than an
Nvidia Tesla K80, a popular datacenter GPU. The speed
of a GPU is taken into account while calculating the
normalized costs above. If a neural network takes 30
days to complete training on an AWS p2.xlarge
instance, it will cost $648. On Vectordash, the exact
same neural network can be trained to completion in
just 5.5 days for $38.78.
CloudProvider
InstanceType TFLOPs
DailyPrice
Normalizeddaily price
Normalized30-day price
Vectordash Nvidia 1080 ti 11.34 $7.11 $1.29 $38.70
AmazonWeb Services
p2.xlarge 2.9 $21.60 $21.60 $648.00
GoogleCloud Nvidia Tesla K80 2.9 $10.80 $10.80 $324.00
MicrosoftAzure NC6 2.9 $21.60 $21.60 $648.00
GPU Instance Pricing (as of 2/11/2018)
16.7x cheaper than Amazon Web Services
16.7x cheaper than Microsoft Azure
Significantly faster than Amazon, Google, and Microsoft
The price of instances is dynamically adjusted based on
the supply and demand of compute. Instead of being a
monopolistic cloud provider charging arbitrary prices,
we let our users decide what compute should be worth.
A marketplace for compute
Lack of compute is a limiting factor for AI development
Accelerate AI breakthroughs with Vectordash
“One very easy way of always getting our models
to work better is to just scale the amount of
compute. So right now, if we’re training on, say, a
month of conversations on Reddit, we can,
instead, train on entire years of conversations of
people talking to each other on all of Reddit.”
— Andrej KarpathyDirector of AI at Tesla
“There’s somewhat of a linear connection between
how much compute power one has, and how many
experiments one can run. How many experiments
one can run determines how much knowledge you
acquire or discover.”
— Trevor DarrellCo-Director of Berkeley AI Research Lab (BAIR)
“The surface of AI problems we can solveis limited by the hardware we have available.”
— Greg Brockman, co-founder of OpenAI
AI has the potential to become the most impactful technology humanity will ever create, giving us
the ability to solve problems once thought to be unsolvable. However, the lack of available GPU
compute remains to be the limiting factor in developing AI systems. Larger neural networks often
require multiple GPUs to train on, taking up to weeks at a time. Novel ideas are often discarded
simply because the GPU compute costs would be too high.
By enabling latent compute to be brought to market, we’ve been able to reduce GPU instance
costs by a factor of 10x. Our goal is to provide everyone with high performance compute — not
just top research labs and large organizations. More affordable compute directly translates to an
increase in groundbreaking ideas being developed, improvements upon existing ideas, and the
true democratization of AI.
Vectordash reduces GPU compute costs by 10x
References
Amazon EC2 Pricing – AWShttps://aws.amazon.com/ec2/pricing/
Dettmers, Tim. “Which GPU(s) to Get for Deep Learning: My Experience and Advice for Using GPUs in Deep Learning.” Tim Dettmers, 4 Sept. 2017, timdettmers.com/2017/04/09/which-gpu-for-deep-learning/.
Graphics Processing Unit (GPU) | Google Cloud Platformhttps://cloud.google.com/gpu/
Linux Virtual Machines Pricinghttps://azure.microsoft.com/en-us/pricing/details/virtual-machines/linux/
McHugh, Jim. “NVIDIA Brings DGX-1 AI Supercomputer in a Box to OpenAI | NVIDIA Blog.” The Official NVIDIA Blog, 14 Oct. 2016, blogs.nvidia.com/blog/2016/08/15/first-ai-supercomputer-openai-elon-musk-deep-learning/.
Mooney, Chris, and Steven Mufson. “Why the bitcoin craze is using up so much energy.” The Washington Post, WP Company, 19 Dec. 2017, https://www.washingtonpost.com/news/energy-environment/wp/2017/12/19/why-the-bitcoin-craze-is-using-up-so-much-energy/?utm_term=.0329a6c90dce
NVIDIA Delivers AI Supercomputer to Berkeley | NVIDIA BlogJim McHugh - https://blogs.nvidia.com/blog/2016/12/06/ai-supercomputer-berkeley/
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