The AI ​​hype has been phenomenal in recent years. Walking down the street, scrolling through your phone, you see terms like "large models," "deep learning," and "computing power" everywhere. This has led to a widespread misconception: to work on AI, you absolutely need GPUs, preferably NVIDIA—one isn't enough, two are enough, eight are enough. This isn't entirely wrong, but taking it as the sole answer is simplistic. Whether or not an AI server needs a GPU depends on your specific role in AI, the tasks it will perform, and your available budget.

　　Let's start by explaining how GPUs have achieved their legendary status in AI. GPU stands for Graphics Processing Unit, initially designed to handle complex visuals in computer games. Its biggest difference from a CPU lies in its architecture—CPUs excel at complex, sequential logic processing, like a skilled craftsman proficient in various tasks, doing precise work but only one or two at a time. GPUs, on the other hand, have hundreds or thousands of relatively simple computing cores, like a group of workers on an assembly line. While individual workers may have limited capabilities, the sheer number of them working simultaneously makes them incredibly efficient at handling tasks that can be broken down into countless smaller parts. The core computations of deep learning and neural networks—matrix multiplication and convolution operations—are precisely the kind of tasks that can be scaled up by sheer numbers. A training run that can be completed in a few days on a GPU using thousands of cores simultaneously might take a CPU several months. Therefore, the mainstream choice for AI computing power is supported by sound mathematical and hardware logic, which is undeniable.

　　However, simply equating an "AI server" with a "GPU server" is like equating a "car" with a "race car." A race car is indeed fast, but it's not the optimal solution for tasks like grocery shopping, picking up children, or hauling a load of goods. The field of AI is vast. From training a large model to enabling a smart speaker to understand "What's the weather like today?", the computational demands and cost levels involved span several orders of magnitude, making generalizations impossible.

　　Let's first look at AI training. Training a large language model, like GPT, is indeed inseparable from GPUs—not just one or two, but clusters of tens of thousands of high-performance GPUs, coupled with high-speed interconnects and dedicated storage systems. In this scenario, GPUs are essential because the computational load during training is extremely massive, requiring extreme parallel computing capabilities, which current CPUs simply cannot handle. However, the problem is that only a handful of tech giants and top research institutions worldwide use AI servers of this caliber. The vast majority of individuals and businesses will never train a model with billions of parameters. Most people are fine-tuning, using open-source pre-trained models, feeding them their own business data, and running a few epochs to adapt them to their specific scenarios. In this process, GPUs still have an advantage, but the barrier to entry has lowered. An RTX 4090 or even an RTX 4060 can handle it. If you're not time-sensitive, running it on a CPU isn't entirely impossible, just slower.

　　Now let's talk about AI inference. Inference is the process of a trained model outputting results when it's actually used, taking a sentence or an image as input. The computational load of inference is far less than training, but the requirements for latency and concurrency are much higher. In many inference scenarios, GPUs are not the optimal choice. For example, if you're creating an intelligent customer service system where the model needs to respond to a user's question within two or three seconds, the computational load of a single inference is actually not large, and a CPU can handle it perfectly well. Moreover, CPU servers are cheap, stable, and have a mature ecosystem. If you're developing a high-concurrency API service with hundreds or thousands of requests coming in simultaneously, you might need a GPU to handle the concurrency. However, you'll find that GPU memory capacity often becomes a bottleneck; a single card can only handle a limited number of concurrent requests, making the cost per request potentially higher than using a CPU. Therefore, many large internet companies use CPUs with lightweight acceleration solutions when providing inference services, rather than blindly piling on GPUs.

　　Besides CPUs and GPUs, the hardware options for AI servers are actually quite diverse. FPGAs are one such option; they act like a "semi-custom" chip that can be repeatedly programmed. If you have a specific AI algorithm to run, you can directly program the algorithm into the FPGA's hardware circuitry, eliminating the overhead of instruction sets, resulting in extremely high efficiency and significantly lower power consumption than GPUs. The problem with FPGAs is the high development threshold; you need to understand hardware description languages, and debugging can be quite painful. They are suitable for scenarios with large volumes and relatively fixed algorithms, such as using FPGAs in data centers for video transcoding and accelerating AI inference. Another type is ASIC, or Application-Specific Integrated Circuit, most notably Google's TPU. These chips are designed from the outset to run specific AI calculations, offering higher efficiency and lower power consumption than GPUs, but their drawback is a lack of versatility; they may not be suitable for different network architectures. While running TensorFlow models on a TPU on Google Cloud provides an excellent experience, outside of that ecosystem, the applications for TPUs are limited.

　　Another option often underestimated is the CPU itself. Don't assume that running AI on a CPU is necessarily "unusable." In recent years, both Intel and AMD have integrated AI acceleration instruction sets into their CPUs, such as Intel's AVX-512 VNNI and AMX, and AMD's AVX-512 VNNI and AI acceleration units. These instruction sets can significantly improve the speed of CPUs when processing low-precision inference. If you are running lightweight models, such as small models for object detection or speech recognition, or performing offline batch processing tasks that are not sensitive to latency, a CPU server is perfectly adequate. Moreover, CPU servers are inexpensive, easy to maintain, and don't require worrying about driver and CUDA version compatibility issues. Many companies, when implementing AI, place training on GPUs and inference on CPU clusters. This ensures both training efficiency and controls inference costs, making it a relatively mature solution.

　　From a budget perspective, whether or not to use a GPU largely depends on your budget. A single, high-quality data center GPU, such as the A100 or H100, costs as much as several high-performance CPU servers. Furthermore, GPU servers require higher power consumption, more robust cooling, and dedicated chassis and motherboards, resulting in an overall cost that is at least three to four times that of CPU servers. If you are a startup team or simply want to experiment with adding AI functionality to your business, starting with GPU servers can put a significant financial strain on your resources. It's better to first test the prototype using CPUs and then use on-demand GPU instances provided by cloud service providers for testing. Only consider purchasing GPUs when your business volume increases and you genuinely need them.

　　Another easily overlooked point is the capability of your technical team. GPU servers aren't simply plug-and-play solutions. Incompatibility in driver versions, CUDA versions, cuDNN versions, and deep learning framework versions can all cause them to fail or experience significant performance degradation. If your team lacks someone familiar with GPU development and maintenance, troubleshooting the environment alone can take weeks. CPU server environments, on the other hand, are much more mature and stable, easily managed by any Linux-savvy administrator. Given limited technical resources, choosing CPU servers or using cloud-hosted AI services is far more reliable than building your own GPU cluster.

　　Furthermore, the specific needs of your scenario must be considered. In edge computing scenarios, AI servers are typically deployed in factory workshops, retail stores, and near security cameras, where space, power supply, and heat dissipation are limited. Using a GPU that consumes 200-300 watts is impractical in these situations. Instead, low-power AI accelerator cards, such as NVIDIA's Jetson series, Intel's Neural Compute Stick, or even running lightweight models on an ARM-based CPU, are more reasonable choices. While these solutions may not have the peak computing power of a large GPU, they excel in low power consumption, small size, and strong environmental adaptability, making them truly applicable to real-world scenarios.

　　In short, AI servers don't necessarily need GPUs. It depends on the task you're running, your budget, and your team's maintenance capabilities. For training large models and performing high-concurrency real-time inference, GPUs are indispensable. However, for lightweight inference, offline batch processing, edge deployment, or simply testing the waters, CPUs, FPGAs, TPUs, and even ARM processors all have their uses. The biggest mistake when choosing hardware is blindly chasing high-end options. Others use eight H100 GPUs to train models with hundreds of billions of computers; that's their need, not yours. You only need to figure out how much computing power your business requires, how much you're willing to spend, and what technologies your team can handle. Then, within that framework, find the "just right" solution, and you'll already be a winner.