In today’s world where ChatGPT writes copy, self-driving cars avoid obstacles, and smartphone photo albums automatically recognize faces, “Artificial Intelligence (AI)” is no longer an unfamiliar term. However, few people notice the “unsung hero” supporting these intelligent scenarios—AI Computing (Artificial Intelligence Computing). It is like the “engine” of AI; without powerful computing support, even the most sophisticated algorithms cannot be put into practice. Today, we will use plain language to break down the core logic of AI Computing and its in-depth connection with AI and Machine Learning (ML).

What Exactly Are AI, ML, and AI Computing?

To understand AI Computing, we must start with the most basic definitions—many people confuse AI, ML, and AI Computing, but they actually form a three-tier relationship of “goal-method-tool”.

1. Artificial Intelligence (AI): The “Intelligent Outcome” We Ultimately Pursue

The essence of AI is enabling machines to imitate human thinking and complete “intelligent tasks” that were originally only possible for humans. For example:

  • Understanding language, such as ChatGPT’s conversations and voice assistants converting speech to text;
  • Recognizing objects, such as smartphone QR code scanning and self-driving cars “seeing” traffic lights;
  • Making decisions, such as e-commerce platforms recommending “products you may like”.

Simply, AI is the “goal”—the final result of our desire to make machines “smart”.

2. Machine Learning (ML): The “Core Method” to Achieve AI

To make machines smart, we cannot teach them sentence by sentence like we teach children (e.g., “This is a cat, that is a dog”); after all, the amount of data is too large to handle. Thus, scientists invented Machine Learning (ML): allowing machines to automatically summarize rules by “learning from data”, rather than relying on manual programming.

Take an example: To enable a machine to recognize “cats”, there is no need to tell it “cats have pointed ears and long tails”. Instead, we feed it 100,000 images of cats and 100,000 images of non-cats. The machine will independently analyze the pixel features of these images (such as fur color distribution and contour shape) and summarize the rules of “what a cat is”—this “rule-summarizing process” is the core of ML.

Therefore, ML is the “method”—the key technical path to achieve the goal of AI.

3. AI Computing: The “Computing Foundation” Supporting ML and AI

When we use ML to train machines, we face a key problem: the enormous volume of data and computations. For instance, training a model to recognize cats may require processing hundreds of millions of pixel data; training a large language model like ChatGPT requires processing trillions of words of data and performing complex mathematical operations (such as matrix multiplication and tensor transformation).

The CPU (Central Processing Unit) of an ordinary computer simply cannot handle such “high-intensity work”—it is like using a family car to haul dozens of tons of goods, which is neither efficient nor feasible. This is where AI Computing comes in: it is a set of “computing systems specifically designed for AI tasks”, including hardware (such as GPUs and NPUs), software (such as the TensorFlow framework), and workflows. Its core function is to “efficiently process the massive data and complex computations required for AI/ML”.

In a nutshell: AI Computing is the “tool”—the computing infrastructure that enables the implementation of ML methods and the achievement of AI goals.

The Relationship Between the Three:

Many people wonder “which one includes the others among AI, ML, and AI Computing”. In fact, their relationship is more like a “pyramid”:

  • Top tier: AI (Goal): The “intelligent outcome” we pursue, such as machines being able to converse and recognize objects;
  • Middle tier: ML (Method): The core technology to achieve AI, specifically by training models through data;
  • Bottom tier: AI Computing (Tool): The computing foundation supporting ML operations, responsible for processing data and computations.

Take a vivid example: If we compare “AI” to “building an intelligent building”, then “ML” is the “construction plan” (e.g., determining which materials to use and how to build the framework), and “AI Computing” is the “construction equipment such as excavators and cranes”—without equipment, even the most perfect plan cannot turn the building into reality.

In other words: Without AI Computing, ML cannot operate efficiently; without ML, AI can only remain in the elementary stage of “manual programming” and cannot achieve true “intelligence”.

The Workflow of AI Computing

AI Computing is not a “one-click generation of intelligence” but a coherent process—like a factory production line that processes “raw materials (data)” into “finished products (intelligent models)”. It is specifically divided into 5 steps:

1. Data Preparation

The “intelligence level” of an AI model depends on the quality of the data it “feeds on”. The core of this step includes:

  • Data collection: Acquiring raw data related to the task. For example, when training a “face recognition” model, we collect millions of face images of different people;
  • Data cleaning: Removing useless or incorrect data, such as blurry images and mislabeled “face-non-face” data;
  • Data labeling: Adding “labels” to the data, such as marking a photo with “This is Zhang San” or “This is Li Si”, so that the machine knows “what to learn”.

This step is like “washing and cutting vegetables”—if the raw materials are not fresh or properly processed, the final “dish (model)” will definitely not be good.

2. Model Training

This is the core and most computing-intensive step of AI Computing. Simply put, it is letting the machine “learn rules from data” using ML algorithms:

  • First, build a “blank model”, such as a simple neural network (similar in structure to the neurons of the human brain);
  • “Feed” the prepared data to the model and let the model perform repeated computations (e.g., comparing the differences between “input data” and “correct labels” through matrix multiplication);
  • Continuously adjust the model’s parameters (e.g., adjusting the connection strength between neurons) until the model can make accurate predictions—for example, when seeing a new photo, it can recognize “This is Zhang San” with 99% accuracy.

Take an example: Training a model to recognize “cats” may require running on a GPU cluster for several days or even weeks—during this period, billions of computations are processed, which is the “main battlefield” of AI Computing.

3. Model Optimization

A trained model may be “large and slow”; for example, a large language model may have tens of billions of parameters, which cannot be installed on an ordinary smartphone. The core of this step is “slimming down” the model:

  • Model compression: Reducing the number of parameters (e.g., simplifying “1.2345” to “1.2”), which causes minimal loss of accuracy but significantly reduces the model size;
  • Computation quantization: Lowering the computation precision (e.g., using “8-bit integers” instead of “32-bit floating-point numbers”), which can increase the computation speed by 4 times.

An optimized model can not only maintain accuracy but also run on edge devices such as smartphones and cameras—it is like transforming a “large truck” into a “sedan”, which is suitable for driving on urban roads (edge scenarios).

4. Model Deployment

Install the optimized model on actual devices to handle real tasks:

  • Cloud deployment: For example, ChatGPT’s model is deployed on Microsoft Azure’s GPU cluster, and users around the world can access it via the internet;
  • Edge device deployment: For example, a smartphone’s face recognition model is deployed on the local NPU (Neural Processing Unit), enabling real-time unlocking without an internet connection;
  • Industrial device deployment: For example, a factory’s quality inspection model is deployed on the AI chip of a camera to recognize product defects in real time.

This step is like “handing the finished tool to the worker”—the model finally moves from the “laboratory” to “practical scenarios”.

5. Model Monitoring and Iteration

A model is not “a one-time achievement”. For example, a model designed to recognize “garbage categories” will “fail to recognize” new types of garbage (such as new plastics). This step requires:

  • Performance monitoring: Tracking the model’s accuracy (e.g., monitoring whether the recognition error rate increases);
  • Data updating: Collecting new scenario data (e.g., images of new types of garbage);
  • Retraining: Retraining the model with new data to enable it to “learn” to recognize new things.

This is like “upgrading the tool”—allowing the model to always adapt to changing needs.

Applications of AI Computing

You may not realize it, but AI Computing has already penetrated every aspect of our lives and work, mainly divided into two major scenarios: “consumer-level” and “industrial-level”.

1. Consumer-Level Applications: Changing Our Daily Lives

  • Smart terminals: Smartphone face unlocking relies on NPUs to process camera data in real time; photo retouching uses GPUs to quickly compute image optimization;
  • Entertainment and media: The “recommendation algorithms” of short video platforms rely on cloud GPU clusters to analyze your viewing data and recommend content you may like; AI special effects in movies use GPUs to render complex scenes;
  • Smart homes: Voice recognition of smart speakers uses edge AI chips to process sound data, enabling responses without an internet connection; path planning for robot vacuum cleaners uses ML models and computing power to avoid obstacles in real time.

2. Industrial-Level Applications: Driving the Efficiency Revolution in Industries

  • Manufacturing: Factory “AI quality inspection” uses cameras and AI chips to recognize product defects in real time, with an accuracy rate 30% higher than manual inspection and 10 times higher efficiency; intelligent scheduling uses ML models to analyze production data, optimize production plans, and reduce downtime by 20%;
  • Healthcare: AI medical image diagnosis uses GPUs to train models for analyzing CT scans and X-rays, enabling the detection of early lung cancer signs 3 months earlier than doctors; AI drug research and development uses computing power to simulate molecular structures, shortening the new drug development cycle from 10 years to 3 years;
  • Financial services: AI anti-fraud systems analyze user fund transfer data in real time to identify abnormal transactions, increasing the interception rate by 50%; intelligent investment advisory services use ML models to analyze market data and provide personalized investment recommendations for users;
  • Transportation and logistics: Self-driving cars rely on on-board GPUs/NPUs to process massive data from lidars and cameras in real time, making millions of decisions per second; logistics route optimization uses ML models to analyze orders and road conditions, reducing transportation costs by 15%.

Core Advantages of AI Computing

Compared with traditional computing (e.g., processing data with CPUs), the advantages of AI Computing can be summarized as “three fast and one economical”.

1. Fast Processing Speed: “Second-Level Response” for Massive Data

Traditional CPUs excel at “single-task, sequential processing” (e.g., opening a document or calculating a spreadsheet), but they “lag” when faced with AI’s “parallel tasks” (e.g., processing 1,000 images simultaneously). In contrast, the core hardware of AI Computing (such as GPUs) has thousands of computing cores and can process massive data simultaneously—for example, training an image recognition model with a GPU is 50-100 times faster than with a CPU; real-time recognition of a photo takes only 0.01 seconds.

2. Strong Adaptability: From “Fixed Programming” to “Flexible Learning”

Traditional computing relies on “manual programming”—for example, a calculator can only perform addition, subtraction, multiplication, and division because programmers have written the corresponding code in advance. However, ML models supported by AI Computing can “independently learn” new rules through data—for example, an AI customer service robot does not require programmers to write new code every time; it can learn to answer new questions just by being fed new conversation data.

3. High Decision Accuracy: Reducing “Human Errors”

When humans process data, they are easily affected by fatigue and emotions—for example, the error rate of factory inspectors increases after 8 hours of continuous work. In contrast, models supported by AI Computing can maintain stable accuracy as long as there is sufficient data and adequate training—for example, the error rate of AI medical image diagnosis is approximately 0.5%, which is much lower than the 5% error rate of human doctors (especially for early tiny lesions).

4. Economical in the Long Run: From “Labor-Intensive” to “Computing-Driven”

Although the initial investment in AI Computing (such as GPU hardware and software frameworks) is relatively high, it can significantly reduce costs in the long run. For example:

  • An AI quality inspection system requires a one-time investment of approximately 500,000 yuan, but it can replace 10 inspectors (each with an annual salary of 100,000 yuan). The cost can be recovered in 2 years, and it can save 1 million yuan annually thereafter;
  • An AI customer service robot can replace 5 human customer service representatives, work 24 hours a day without interruption, and does not require social security or leave—its long-term cost is only 1/10 of that of human labor.

How Can Enterprises Use AI Computing to Improve Work Efficiency?

For enterprises, AI Computing is not a “high-end gimmick” but a practical “efficiency tool”. Specifically, enterprises can start from the following 3 core directions.

1. Automating Repetitive Work: Freeing Employees from “Mechanical Labor”

Eighty percent of the basic work in enterprises is “repetitive and rule-based” (such as data entry, document review, and customer consultation)—these tasks are most suitable for automation using AI Computing.

  • Case 1: Bank Data Entry: Traditionally, employees need to manually enter customer ID card and bank card information, processing 500 entries per day with a 5% error rate. After using AI Computing combined with OCR recognition and NPU processing, the system can recognize 10 entries per second with a 0.1% error rate and work 24 hours a day—it is equivalent to the workload of 10 employees but only requires 1/5 of the cost.
  • Case 2: E-Commerce Customer Service: Traditional customer service representatives need to manually reply to repetitive questions such as “order inquiries” and “logistics consultations”, leading to long waits during peak hours. After using AI customer service robots combined with large language models and cloud GPUs, 80% of common questions can be answered in real time, and only complex questions are transferred to humans—the efficiency of the customer service team is tripled, and the customer waiting time is reduced from 10 minutes to 1 minute.

2. Optimizing Decision-Making Processes: Replacing “Experience-Based Judgment” with “Data-Driven Insights”

Many enterprises rely on “managers’ experience” for decision-making (e.g., “I think this product will sell well”), which is prone to errors. AI Computing can provide accurate decision support by analyzing massive data.

  • Case 1: Retail Inventory Management: Traditionally, store managers rely on experience to stock goods, often resulting in “out-of-stock bestsellers and overstocked slow-moving products”. After using AI Computing combined with ML models to analyze historical sales data, weather, and holidays, the system can accurately predict the sales volume of each product—the inventory turnover rate is increased by 20%, and overstocked inventory is reduced by 30%.
  • Case 2: Manufacturing Equipment Maintenance: The traditional approach is “repairing after a breakdown”, which causes significant downtime losses. After using AI Computing combined with sensors to collect equipment data and ML models to predict failures, the system can predict equipment failures 3 days in advance and arrange planned maintenance—downtime is reduced by 40%, and maintenance costs are lowered by 25%.

3. Innovating Business Models: From “Traditional Services” to “Intelligent Value-Added Services”

AI Computing can not only optimize existing work but also help enterprises develop new businesses and services to create additional revenue.

  • Case 1: Traditional Automakers → Intelligent Automakers: Traditional automakers rely on car sales for profits, with thin margins. After using AI Computing combined with on-board GPUs/NPUs to support autonomous driving and intelligent cockpits, they have launched “autonomous driving services (charged by kilometer)” and “intelligent navigation value-added services (real-time recommendations for restaurants and parking lots)”—the proportion of additional revenue has increased from 0 to 15%.
  • Case 2: Traditional Hospitals → Intelligent Healthcare: Traditional hospitals rely on diagnosis and treatment fees for revenue, with limited service capabilities. After using AI Computing combined with GPUs to train medical image models, they have launched “remote AI diagnosis services (providing image analysis for primary hospitals)” and “AI chronic disease management services (real-time monitoring of patient data and medication reminders)”—the service scope has expanded from local to national, and patient satisfaction has increased by 30%.

Conclusion

From smartphone unlocking to autonomous driving, and from customer service robots to AI-driven drug research and development, AI Computing is quietly transforming our lives and work. WhaleFlux, an intelligent GPU resource management tool specifically designed for AI enterprises, has become a key enabler for the implementation of AI Computing. Leveraging its intelligent GPU resource management capabilities, it optimizes the utilization of multi-GPU clusters and provides high-end GPUs such as NVIDIA H100 and H200 — it is not an “unreachable black technology” but a “practical tool” that helps enterprises reduce cloud computing costs, improve model deployment efficiency, and at the same time enables individuals to enjoy more stable intelligent services.

In the future, with the reduction of computing power costs and the optimization of AI models, AI Computing will penetrate more scenarios, such as “AI teachers” providing personalized tutoring and “AI farmers” conducting precision crop cultivation. WhaleFlux will also continue to support these new scenarios through flexible GPU rental plans (with a minimum rental period of one month) and efficient computing power scheduling; for us, while understanding the core logic of AI Computing, leveraging tools like WhaleFlux will better help us seize the new opportunities driven by “computing power”.