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In today's digital era, computing power has become a key force in promoting economic development and scientific and technological innovation. However, to make the most of computing power, we need to understand the cost structure behind it, which is crucial for businesses and individuals when deciding whether to invest in computing power resources.

This article mainly provides you with a comprehensive understanding of the composition of computing power cost and how to calculate it from the following three perspectives.

1. Introduction to the composition of computing power cost

2. How to effectively control computing power costs

3. Computing power cost calculation cases

1. Introduction to the composition of computing power costs

Hardware procurement costs

The basis of computing power is hardware equipment, such as servers, CPUs, GPUs, etc. The price of hardware with different performance and specifications varies greatly. For example, high-performance GPUs often cost several times more than regular CPUs. Moreover, as technology continues to update, the price of hardware will fluctuate.

Taking a small data center as an example, if you want to configure a batch of servers that can provide a certain amount of computing power, hardware procurement alone may require hundreds of thousands of yuan or even more. For large-scale data centers or cloud computing service providers, hardware procurement costs are astronomical.

Operational costs

The proper functioning of hardware equipment requires ongoing maintenance and management. This includes the maintenance of the equipment's cooling system, power supply system, and software system updates and optimizations. Cooling systems are essential to keep hardware at the right operating temperature, which consumes a lot of power and maintenance costs. At the same time, in order to ensure the stability and security of the system, regular software updates and vulnerability fixes are also essential, which requires professional technicians to invest time and effort.

According to statistics, O&M costs usually account for 20% to 30% of the total cost of computing power.

Electricity costs

Computing power equipment consumes a lot of electricity during operation. Especially when performing high-intensity computing tasks, the power consumption is even more staggering. In the case of Bitcoin mining, due to the large amount of computing power required, some mining farms consume electricity equivalent to even a small town. For the data center of the average enterprise, the cost of electricity is also an important expense. Moreover, the difference in electricity prices in different regions can also have an impact on the total cost.

Venue rental costs

If it is a large-scale computing power facility, such as a data center, it needs to occupy a large amount of space. The rental cost of the venue is also part of the cost. In some first-tier cities, high-quality data center space rentals are expensive. Moreover, the location of the site will also affect performance indicators such as network latency, which in turn will affect the service quality and cost of computing power.

Labor costs

From hardware installation and debugging to system operation and maintenance management, professional technical personnel are required. The compensation, training and benefits of these personnel constitute labor costs. A high-quality technical team can ensure the efficient operation of the computing power system, but it will also increase the cost burden of enterprises.

To sum up, the cost of computing power is a complex component system, involving hardware procurement, operation and maintenance, power, site leasing and manpower. When making computing power investment or usage decisions, these factors need to be considered comprehensively to ensure the best cost performance. Only by having a clear understanding of the various components of computing power cost can we better utilize this powerful resource in the digital era to achieve business innovation and development.

2. How to effectively control the cost of computing power

Optimize hardware configuration

● On-demand purchase: Choose the appropriate hardware configuration based on actual needs to avoid overinvestment.

● Used Market: Consider purchasing certified used hardware, which is often significantly cheaper than new.

Improve energy efficiency

● Energy-Efficient Equipment: Choose servers and components with high energy efficiency ratios to reduce energy consumption.

● Dynamic adjustment: Automatically adjust the working status of the equipment according to the load situation, avoiding unnecessary energy waste.

Leverage cloud computing resources

● Elastic scaling: Using cloud services can dynamically adjust resources according to actual needs to avoid high fixed costs.

● Cost optimization strategies: For example, utilizing idle resources (such as Spot Instances) or choosing competitive pricing plans.

Optimization at the software level

● Code Optimization: Optimize algorithms and programs to improve computing efficiency and reduce the required computing power resources.

● Parallel computing: Rationally allocate tasks and use the parallel processing capabilities of multiple cores or nodes to speed up task completion.

Resource sharing and reuse

● Establish an internal computing power resource sharing platform so that idle computing power resources can be shared between different departments or projects to improve resource utilization.

● For some reusable computing tasks or models, save and reuse them to avoid double calculations.

Monitoring and analytics

● Establish a complete computing power cost monitoring system to track the expenditure of various costs in real time.

● Through data analysis, find out the main consumption points and optimization links of costs, and make targeted improvements.

3. Computing power cost calculation cases

Case 1: A small Internet company

The company decided to build its own small data center to handle the growing number of user data and run complex business algorithms.

They purchased 10 medium-performance servers, each priced at about 20,000 yuan, and the total hardware purchase cost was 200,000 yuan.

In terms of operation and maintenance, a full-time operation and maintenance personnel were hired with a monthly salary of 15,000 yuan, plus the maintenance cost of the cooling system and power supply system was about 50,000 yuan per year, and the operation and maintenance cost was about 230,000 yuan per year.

In terms of electricity consumption, due to the all-day operation of the server, the monthly electricity bill is about 8,000 yuan, and the annual electricity bill is about 96,000 yuan.

The venue rental chose a more remote but relatively cheap place, with an annual rent of 50,000 yuan.

In terms of labor costs, in addition to operation and maintenance personnel, there is also a part-time technical support personnel, with an annual cost of about 50,000 yuan.

The total computing power cost in the first year is about 626,000 yuan.

Case 2: A large financial institution

For risk assessment and data analysis for high-frequency trading, the financial institution has established a large data center.

The hardware purchased 100 high-performance servers, each priced at 50,000 yuan, with a total cost of 5 million yuan.

The operation and maintenance team consists of 5 professionals, with an average monthly salary of 20,000 yuan, plus system maintenance costs of about 1.5 million yuan per year.

The electricity consumption is huge, with a monthly electricity bill of up to 200,000 yuan and a year of electricity bill of 2.4 million yuan.

The venue is leased in a high-end data center park in the city center, with an annual rent of 2 million yuan. Labor costs also include technology research and development and management personnel, which is about 3 million yuan per year.

The computing power cost in the first year exceeded 13.9 million yuan.

Case 3: A scientific research institution

The research institute rented cloud computing services for complex scientific calculations and simulation experiments.

According to the calculation needs, the monthly computing power rental cost is about 100,000 yuan.

Due to the large amount of data, the cost of data transmission and storage is about 20,000 yuan per month.

There is no need for hardware procurement and site rental, but in terms of manpower, there are dedicated technicians responsible for communicating with cloud service providers and optimizing computing tasks, with an annual labor cost of about 150,000 yuan.

The total cost of computing power for a year is about 1.59 million yuan.

Notes:

Depreciation expenses: In actual financial calculations, hardware procurement costs are usually spread over several years at a certain depreciation rate. For example, assuming a server has a lifespan of 5 years, the annual depreciation expense will be 200,000 yuan divided by 5 years, which is 40,000 yuan per year. If depreciation expenses are included, the total cost of the first year of Case 1 will be 626,000 yuan plus 40,000 yuan of depreciation expense, that is, 666,000 yuan.

Taxes and other fees: Other fees such as taxes and insurance need to be considered in practice.

Inflation: The impact of inflation on costs also needs to be considered when operating for a long time.