AI painting and video generation are no longer just toys for geeks. Self-media, e-commerce, cross-border content teams, and design studios—almost every industry is experimenting with AI for batch image generation, short video creation, and high-definition restoration. However, most beginners and small to medium-sized teams have serious misconceptions about server selection. Simply renting a regular cloud server, a GPU with low VRAM, or even running AI models on a CPU-based host often results in image errors, resolution limitations, video generation crashes, VRAM overflow, and extremely slow iteration speeds.

AI painting and video generation are GPU computing tasks that are extremely sensitive to VRAM, rely heavily on parallel computing power, and have high I/O throughput requirements. This is completely different from the server requirements for traditional website building, program execution, and data storage. The core bottleneck is not the CPU or memory, but rather the GPU VRAM capacity, CUDA core computing power, and tensor computing performance.

Today, we'll clarify the server selection logic for AI painting and video generation—VRAM determines success or failure; a wrong choice renders all your efforts futile.

Why can't regular servers run AI painting and video generation?

Many users mistakenly believe that AI drawing and AI video are "lightweight graphics rendering tasks" that can be run on ordinary computers with low-end GPUs—this is a classic misconception.

Modern AI generative models are based on diffusion models. Outputting a single 2K/4K high-definition image, redrawing the original image, overlaying multiple LoRA/ControlNet plugins, batch outputting images, and high-definition restoration will instantly consume a large amount of video memory. Insufficient video memory will directly trigger an OutOfMemoryError (OOM)—the program will crash immediately, without even a chance to report an error.

Video generation is even more extreme. AI video is the AI ​​rendering of a continuous frame sequence. A 10-second short video involves iterative calculations of hundreds of frames. After overlaying frame interpolation, dynamic frame replacement, motion models, and high-definition super-resolution, the video memory usage is 3 to 8 times that of static drawing. Low-memory graphics cards can only generate low-definition short videos; long videos and 4K dynamic videos simply cannot run.

CPU servers cannot completely replace GPUs. AI generation involves massively parallel floating-point tensor computation. GPUs, with thousands of CUDA cores, excel at parallel iterative operations; CPUs, with fewer cores and primarily relying on serial computation, suffer from a speed difference of tens of times in running AI models and are highly prone to stuttering and timeouts. All commercial AI content production must rely on NVIDIA CUDA architecture GPUs.

First Priority in Selection: VRAM Capacity – 12GB is the bare minimum, 24GB is the sweet spot.

When choosing a GPU server, don't just look at the graphics card model; VRAM capacity is the top priority.

VRAM Thresholds for AI Painting:

- 8GB and below: Barely runs SD 1.5 basic version; errors occur when enabling high resolution or VAE. Almost never recommended for any serious AI painting work.

- 12GB: Runs SD 1.5 smoothly; SDXL lightweight version is usable. This is the entry-level threshold, representing a graphics card like the RTX 3060 12GB.

- 16GB: Runs SDXL full version, multiple ControlNet overlays, and batch image output. This is the sweet spot, represented by an RTX 4070 Ti 16GB graphics card.

- 24GB: Can run LoRA with full parameter fine-tuning, concurrent generation of 8 images, and FLUX.2. This is the all-rounder, represented by an RTX 4090 24GB graphics card.

SDXL full version can run with 12GB of VRAM but very slowly; 16GB is needed for smooth operation. FLUX.1 requires 12-16GB of VRAM at FP8 precision, and over 24GB at BF16 precision.

VRAM requirements for AI video generation:

AI video generation requires an order of magnitude more VRAM than AI painting. A minimum of 24GB of VRAM is needed, and commercial long videos and 4K dynamic videos require 32GB/48GB of VRAM.

A Sora2-level video generation model, processing 3600 frames of data simultaneously for a 120-second video (30fps), requires a minimum of 80GB/GPU VRAM for basic configurations and over 160GB/GPU for ideal configurations. A single 10-second short video, after frame interpolation and dynamic frame replacement, easily exceeds 24GB of video memory usage.

How to choose a GPU model? From Beginner to Professional: A Table to Understand

Specific selection recommendations:

Personal use: A T4 graphics card is perfectly adequate. If high resolution (1024×1024 or higher) or batch image output is required, an A10 or higher is recommended.

Small to medium-sized teams/studios: The RTX 4090 is a "practical default option"—24GB of VRAM covers most serious image generation tasks, and its cost is far lower than data center-grade GPUs. The NVIDIA L40 is a more professional upgrade option; ComfyUI, Stable Diffusion, and FLUX can all run smoothly within 48GB of VRAM, and most SDXL and FLUX checkpoints can be loaded within 10GB.

Enterprise/commercial mass production: An A100 80GB or H100 is the standard configuration. The RTX PRO 6000 Blackwell server edition features 96GB of GDDR7 VRAM and is designed specifically for enterprise-level AI and graphics applications in data centers.

Besides VRAM, three other key indicators cannot be ignored:

First, the GPU architecture generation. Prioritize the use of the new AdaLovelace or RTX 50 series (Blackwell architecture) architectures. These new architectures support FP16/FP32 high-precision tensor calculations, native AI acceleration, and Transformer optimizations, offering 2-3 times faster speeds, lower power consumption, and greater stability compared to older architectures.

Second, the number of CUDA cores directly determines iteration speed, image output efficiency, and video rendering frame rate. A higher core count results in stronger batch task and multi-instance parallel processing capabilities. The RTX 4090's 16384 CUDA cores are significantly more powerful than the T4's 2560.

Third, memory bus width and bandwidth affect data read/write speeds. High bandwidth can significantly reduce frame rendering latency and stuttering, making it a key parameter for high-definition video generation.

Storage and Network: Easily Overlooked "Hidden Costs"

In terms of storage, AI painting and video generation require far more storage than ordinary websites. Model files (Checkpoints, LoRAs, VAEs, Embeddings) can quickly consume tens or even hundreds of gigabytes of storage space. The storage requirements for video generation models are staggering—the weight files for a single model can reach tens of gigabytes, and training datasets can easily be petabytes in size. Servers must be equipped with large-capacity SSDs or NVMe hard drives; otherwise, model loading and data processing will become new bottlenecks.

Regarding networking, if you need team sharing or remote access via a WebUI, a low-latency network connection directly determines the user experience. Hong Kong nodes, being close to mainland China, offer faster upload speeds for prompts and download speeds for generated images.

Local Deployment vs. Cloud GPU Servers: Which to Choose?

Local Deployment: Suitable for scenarios with long-term, high-frequency use and high data privacy requirements. However, the entry barrier is extremely high—an RTX 4090 single card costs over ten thousand yuan, and a complete system configuration easily exceeds 20,000-30,000 yuan. Moreover, once a local graphics card is purchased, it's fixed and cannot be flexibly expanded.

Cloud GPU Servers: Suitable for scenarios with limited budgets, variable usage frequency, and the need for team sharing. GPUs are rented by the hour; no money is charged when not in use. RTX 4090 cloud instances are available for hourly rates as low as $0.67, with domestic platforms offering RTX 4090 instances starting at just 1.86 RMB per hour. For individual creators and small teams using the service intermittently, cloud deployment offers significantly better value than on-premises purchase.

At the network level, Jtti's Hong Kong node utilizes a bidirectional CN2 GIA direct connection to mainland China, achieving latency as low as 12-18ms in South China. For teams requiring remote access to the WebUI for AI creation, low latency translates to a smoother user experience and faster image download speeds. Furthermore, Jtti offers flexible pay-as-you-go and long-term rental options.

At the storage level, it is recommended that GPU cloud servers be equipped with high-speed NVMe SSDs to ensure efficient model loading and data read/write operations. The Hong Kong T3+ self-operated data center provides enterprise-grade hardware redundancy and 24/7 operational support.

Selection Recommendations: Individual creators and small teams can start with T4 or RTX 4090 instances; studios requiring high-definition batch image or video generation are advised to choose A10 or L40; for enterprise-level commercial mass production scenarios, A100 or H100 are standard configurations.

Choosing the right server is more important than mastering the prompts.

For AI painting and video generation, VRAM is paramount. 12GB is the minimum, 16GB is the sweet spot, 24GB is all-encompassing, and for video generation, start with 24GB. Choosing the wrong server will render even the best prompts and meticulously tuned workflows useless due to OOM errors.

For individual creators and small teams, cloud GPU servers are currently the most cost-effective option—no need for a one-time investment of tens of thousands of yuan in hardware; pay by the hour, only for what you use. After all, the speed and quality of AI generation are never just a matter of "how well the prompts are written"—choosing the right server doubles efficiency; choosing the wrong one reduces everything to zero.