GPU servers have gradually changed from "high-performance luxury" to hard-core productivity tools. In addition to graphics cards, another important hardware that cannot be ignored is memory. No matter how powerful the graphics card is, if the memory is wrong, it may also be full of bottlenecks and poor performance. So the question is: how to choose memory for GPU servers?

　　GPU memory ≠ system memory. GPU video memory is used for temporary storage of data for graphics card operations such as image rendering and deep learning. System memory is the motherboard memory bar, which is used for operating systems and program running. In GPU GPU servers, the two are not interchangeable, but highly coordinated.

　　If the system memory is insufficient, even if the graphics card has powerful performance, data cannot be efficiently scheduled, and the CPU and GPU are waiting for memory resources at the same time, the system is prone to Swap and I/O blocking, and performance will drop significantly. Therefore, choosing the right memory configuration for the graphics card server is the key link to unleash the potential of the entire machine.

　　Let's take a look at how to properly match memory and graphics cards in different application scenarios:

　　Deep learning training/inference server: The most commonly used graphics cards for this type of application are NVIDIA A100, H100, 3090, 4090, etc., which are characterized by large video memory and strong parallel computing.

　　Recommended combination:

　　1 A100 (40GB) → at least 128GB system memory

　　2 4090s (48GB video memory in total) → at least 128~192GB memory

　　Multi-card cluster → 64~96GB memory per card (experience value)

　　Reasons:

　　Training data is usually preloaded into system memory;

　　Insufficient memory will lead to frequent reading from disk, and the training speed will drop sharply;

　　Dataloader/cache/multi-threaded scheduling all consume RAM.

　　Rule: System memory ≈ video memory × number of cards × 2~3

　　Video rendering/graphics workstation: This type of server is commonly paired with NVIDIA RTX series (A6000, Quadro RTX), or AMD W series graphics cards.

　　Recommended configuration:

　　Single RTX A6000 (48GB) → 96GB memory is recommended;

　　Multi-graphics rendering array → system memory is at least twice the total amount of video memory.

　　Reasons:

　　Graphics data and texture resources occupy a large amount of RAM;

　　Adobe After Effects, Blender, Cinema 4D and other tools consume high memory when loading data;

　　Cache, preview frames, etc. are all resident in memory.

　　Game cloud server/video streaming platform: Take the deployment of Unreal, Unity real-time rendering engine or cloud gaming scenarios as an example:

　　Recommended configuration:

　　Each high-end gaming graphics card (such as RTX 3080) is equipped with 32~64GB memory;

　　Multi-user concurrency/multi-container → memory starts at at least 128GB.

　　Reason:

　　Each container under multi-channel concurrency requires independent resource isolation;

　　Virtualization platform (such as Docker + GPU Pass-through) itself occupies memory.

　　Scientific computing/high-performance computing: Such applications often use computing graphics cards such as Tesla, A100, MI200, etc.

　　Recommended configuration:

　　Each GPU is equipped with 64GB memory;

　　Computation-intensive tasks (molecular dynamics, meteorological simulation, etc.) require 128GB+.

　　Additional suggestions:

　　Choose ECC memory to prevent data errors in long-term calculations;

　　Multi-CPU, multi-channel configuration to improve memory bandwidth.

　　The more memory a graphics card server has, the better it is. Many people think that "the bigger the memory, the better", but in fact: too little memory will cause obvious bottlenecks, and too much memory will waste costs and power consumption. The best state is "high frequency + low latency + sufficient capacity".

　　The graphics card determines the computing power, but the memory determines whether it can "exert full power". Choosing the right memory for your GPU server is a game of engineering judgment, cost control, and performance pursuit. On-demand configuration and balanced structure are the smart choices for the future.