[Summary]
AI calculation chips are an important technology that is at the core of the generative AI boom. In particular, understanding the differences in the roles of GPU, ASIC, and NPU will make it easier to see the flow of the entire AI industry.
In this article:
- Basic structure of AI calculation chip
- Why GPU is strong
- Importance of HBM and power consumption
- Difference between learning and inference
- Future market trends
organized for beginners.
What is AI math chip?
An AI calculation chip is a semiconductor that performs AI learning and reasoning at high speed.
Normal CPUs are good at "processing things in order." On the other hand, AI requires the simultaneous processing of large amounts of data.
Therefore, it is important that
- parallel operations
- fast memory
- low power consumption
It is.
In particular, in generative AI, since huge models and large amounts of data are handled, the performance of the CPU alone tends to be insufficient.
Why GPUs have become the mainstay of the AI era
What is GPU?
GPU stands for "Graphics Processing Unit". Originally, it was a semiconductor for game images and 3D graphics.
However, it currently plays a central role in AI calculations.
Why is GPU powerful?
The reason is "simultaneous processing ability".
A comparison of CPU and GPU is as follows.
| Item | CPU | GPU |
|---|---|---|
| Specialty field | Sequential processing | parallel processing |
| Number of cores | Few | very many |
| AI learning | Not suitable | very strong |
In generative AI,
- matrix operations
- vector calculation
- neural network
execute in large quantities.
GPU can process these efficiently.
Furthermore, chip performance is not the only reason why NVIDIA is strong. Development environments centered on CUDA, libraries, cloud environments, and engineer proficiency are major barriers to entry.
Role of ASIC/FPGA/NPU
AI chips are not just GPUs. Depending on the use case, ASICs, FPGAs, and NPUs also matter.
ASIC
ASICs are purpose-specific chips.
Representative examples are below.
- Google TPU
- AWS Trainium
The benefits are
- high speed
- low power consumption
- cost effective
That's it.
On the other hand,
- Less versatile
- Difficult to change design
This is one of the drawbacks.
Suitable for cloud companies that operate in large quantities.
FPGA
FPGA is a semiconductor whose circuit can be rewritten later.
The characteristics are
- highly flexible
- low latency
- Suitable for prototyping
It is to be.
However, it is more difficult to develop and is not as mainstream as GPUs for large-scale AI.
NPU
NPU is a semiconductor often used as an AI chip for smartphones and PCs.
Representative examples are below.
- Apple Neural Engine
- Qualcomm Hexagon NPU
The main uses are
- voice recognition
- Image correction
- local AI
- On-device inference
It is.
Recently, with the spread of "AI PC" and on-device generation AI, the importance of NPUs has increased.
HBM affects AI performance
With current AI semiconductors, computing power alone is not enough.
What is important is the "data supply speed".
HBM is used there.
What is HBM?
High Bandwidth Memory
It means.
AI frequently reads and writes huge amounts of data.
Therefore,
- GPU performance
- memory speed
- connection band
- packaging technology
is important in the set.
As of 2026, HBM3e will be widely used, and the next generation will focus on transitioning to HBM4.
In other words, when looking at the AI chip market, you need to look not only at GPU manufacturers, but also at HBM manufacturers, advanced packages, substrates, and cooling technologies.
From the learning market to the inference market
The current AI market is changing.
Previously, the focus was on "learning" to create giant models.
Currently, "inference" that actually uses AI services is rapidly expanding.
In reasoning,
- low cost
- low power consumption
- High efficiency
- response speed
is emphasized.
Therefore, from now on,
- ASIC
- GPU dedicated to inference
- Edge NPU
- low power memory
may become even more important.
Points that beginners tend to misunderstand
“GPU = AI everything” is not
Although the GPU is the main focus, the overall AI infrastructure
- memory
- communication
- electric power
- cooling
- software
is also important.
Power and cooling can also be physical constraints in AI data centers.
You can't win on performance alone
In the AI chip market,
- software environment
- development tools
- CUDA compatibility
- Cloud provision system
- engineer community
An “ecosystem” such as this is extremely important.
NVIDIA has an advantage in this regard.
Summary
- AI calculation chip is the fundamental technology of the AI era
- GPU has an overwhelming advantage in parallel processing
- ASIC is highly efficient and suitable for cloud companies
- NPU is important for edge AI expansion
- “Inference market” will be a growth theme in the future
In terms of investment and technical understanding,
Look at not only computing power but also memory, power, and software.
is important.
The AI chip market is not just a competition for semiconductors. This is a comprehensive battle involving data centers, cloud, memory, and power infrastructure.
Source/Reference
This article has been reorganized for beginners with reference to official information on AI accelerators, GPU calculations, HBM, and NPU.
- NVIDIA Developer「About CUDA」
- NVIDIA CUDA Programming Guide
- Google Cloud「Tensor Processing Units」
- AWS「AI Accelerator - AWS Trainium」
- Micron「HBM3E」
- Samsung「HBM4」
- Qualcomm AI Engine
- Confirmation date: 2026-05-10