To establish a straightforward hourly rate for each GPU card in the network expressed in dollars. We need to provide a clear, fair and decentralized pricing mechanism for GPU/CPU resources within the Network.
io.net at core is a two-sided marketplace for compute power.
- From the demand side io.net is designed to tackle two major challenges:
- Reduce the high costs associated with renting GPU/CPU computing power, which is essential for scaling AI and ML applications. Therefore we need to consider competitor pricing and availability to offer a competitive and an attractive alternative in the market.
- Address the shortage of GPU nodes to hire in gpu-cloud providers Therefore we will need to adjust pricing on peak times and low availability. Therefore its important to think with the end users in-mind.
- On the supply side we are tapping into two key markets:
One of the advantages of targeting PC gamers is that they are not currently involved in crypto mining, so they do not have expectations of earning money when their resources are not in use. Additionally, they are continually investing in high-end hardware, including the latest GPUs, strong RAM, CPU, memory, and high-end motherboards. This makes them a suitable target for io.net, as they have access to high-speed internet with low ping and are in every city around the world, However, one potential drawback of targeting this market is that they typically only have one GPU card, as opposed to miners who usually have 10 times that amount.
- Crypto GPU miner
They have a large number of GPUs, providing ample resources for io.net's services. Additionally, they already have an interest in crypto mining, making them a natural fit for io.net's IO Worker. They are also facing financial difficulties due to low returns from crypto mining, with most cards earning less than $1per day. However, the drawback is that crypto GPU miners may have poor internet connectivity, as the proof-of-work algorithms they use do not require much bandwidth. They may also have limited storage, with low-capacity flash cards, which leaves little room for io.net's software and packages. Crypto GPU miners are in the business for financial gain and therefore, every investment is calculated. When they purchase new cards, they expect to reach a breakeven point within 9 to 14 months
Pricing Model Components Multi Dimensional Factors
Hardware Performance [ Dollar ]
- A dollar value determined by the compute power of the GPUs.
- Tensor Cores
- GPU Bandwidth
- VRAM [ GRAM ]
- GPU model
- CPU model
- CPU clock speed
- CPU cores
- Disk [SSD/HDD]
To determine those valuations we will have to look at the physical spot card price and compre it to the GPU Cloud providers pricing. GPU Cloud Pricing [ Work in progress ]
Internet Bandwidth [ Multiplier by factor of 1 ]
- Ping in milliseconds
- pricing variations based on the internet bandwidth provided
Competitor prices/ supply availability [ Percent discount multiplier by factor of 1 ]
To take into account the pricing of similar GPUs offered by competitors like AWS and PaperSpace. And Consider the availability of GPUs on competitor platforms to adjust pricing and ensure a competitive edge.
Peak Hours [ Multiplier by factor of 1 ]
Adjust pricing on peak times of the network utilization rate of GPUs.
Commitment Pricing [ Percent discount multiplier by factor of 1 ]
Discounts and incentives for long-term bookings and high-volume users. Two models on-demand are higher in price and long-term contracts benefit from a -50% discount.
Location [ Dollar ]
Different pricing for each country based on factors like electricity costs and local market demand. This will be needed later at large scale when AI/ML Teams need to meet data regulations.
Crypto Earnings [ Multiplier by factor of 1 ]
Whats the best profit a the hardware can make when mining other POW crypto.
Tiered pricing based on duration, speed of GPUs, internet bandwidth, trust score, and geo-location
- How pricing can be decentralized entirely
- How can we make our speedtest.net for the miners hardware, which will benchmark the hardware performance and price it based on the time it takes to complete different types of AI/ML tasks.
Updated about 2 months ago