Vectordash. Vectordash Primer. A GPU accelerated cloud computing marketplace.

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1 Vectordash A GPU accelerated cloud computing marketplace Vectordash Primer contact@vectordash.com

2 Introducing Vectordash Vectordash 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

3 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? Miners across the world earn Bitcoin by solving the Hashcash proof of work.

4 V Vectordash The world s most affordable cloud GPU provider. Backed by a network of powerful Nvidia GPUs. Powerful GPU instances for 10x cheaper 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. Get paid to provide GPU compute 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.

5 Simple. Powerful. Affordable. ssh -i key Welcome to Ubuntu python train.py Step 1 / 300,000, train_loss = Step 2 / 300,000, train_loss = Step 3 / 300,000, train_loss = 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. Ubuntu deep learning image There s no need to spend hours installing drivers and libraries. Instances come with an Ubuntu image preloaded with CUDA, cudnn, and popular machine learning libraries such as TensorFlow, PyTorch, Keras, and Caffe. Vectordash is the simplest way to get started with machine learning, artificial intelligence, and data science.

6 The world s most affordable cloud provider. By far. GPU Instance Pricing (as of 2/11/2018) Cloud Provider Instance Type TFLOPs Daily Price Normalized daily price Normalized 30-day price Vectordash Nvidia 1080 ti Amazon Web Services p2.xlarge Google Cloud Nvidia Tesla K Microsoft Azure NC Vectordash GPU instances are x cheaper than Google Cloud 16.7x cheaper than Amazon Web Services 16.7x cheaper than Microsoft Azure A marketplace for compute 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. Significantly faster than Amazon, Google, and Microsoft 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

7 Accelerate AI breakthroughs with Vectordash Lack of compute is a limiting factor for AI development 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. Vectordash reduces GPU compute costs by 10x 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. 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. 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. Andrej Karpathy Director of AI at Tesla Trevor Darrell Co-Director of Berkeley AI Research Lab (BAIR) The surface of AI problems we can solve is limited by the hardware we have available. Greg Brockman, co-founder of OpenAI

8 References 1. Amazon EC2 Pricing AWS 2. 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/. 3. Graphics Processing Unit (GPU) Google Cloud Platform 4. Linux Virtual Machines Pricing 5. 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/. 6. Mooney, Chris, and Steven Mufson. Why the bitcoin craze is using up so much energy. The Washington Post, WP Company, 19 Dec. 2017, aze-is-using-up-so-much-energy/?utm_term=.0329a6c90dce 7. NVIDIA Delivers AI Supercomputer to Berkeley NVIDIA Blog Jim McHugh -

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