VRAM

In FP16 precision, large models require approximately 2 GB of VRAM per 1 billion parameters.

Sparse MoE means the feed-forward layers are split into multiple experts, and each token activates only a small subset of them, so although the total parameter count is 32.9B, the number of parameters actually computed per forward pass is far smaller than that of a dense model, but VRAM still needs to hold all expert weights.

Video generation models typically require an order of magnitude more computing power than image generation models.

When a single machine hosts multiple models for screenwriting, images, video, speech, and editing, VRAM and inference latency will be the most practical constraints.

The correlation coefficient between VRAM capacity and decoding speed is R=0.41 (moderate correlation), and between total VRAM plus system memory capacity and speed is R=0.13 (essentially no correlation).

Judging whether a model can run by looking only at file size is unreliable; a more prudent approach is to leave a 10% to 20% margin and evaluate dynamically based on the actual task.

A model must be loaded entirely into available VRAM to achieve acceptable running speed.

A graphics card with 16GB of VRAM cannot fully hold a 17GB model; a model either fits entirely into VRAM or it does not.

In local deployment, context length is limited by VRAM, and quantization techniques (such as 4-bit quantization) are usually needed to reduce VRAM usage, but this may sacrifice some precision.

12GB of VRAM is currently a common threshold for entry-level local AI deployment.

VRAM bandwidth is far higher than system memory, making it suitable for storing frequently accessed weights; system memory has large capacity but limited bandwidth, making it suitable as cold storage.

VRAM is the high-speed memory that comes with the graphics card, and model weights, context cache and intermediate computation results all need to be stored in VRAM, so VRAM often becomes the bottleneck for running large models locally earlier than compute power does.

VRAM is one of the most critical bottlenecks when locally deploying large models, as all weight parameters of a large model must be fully loaded into VRAM before inference can run.

The most critical hardware metric for running LLMs such as Qwen3.8 is GPU VRAM — the more the better

During inference, additional VRAM must be reserved for intermediate states such as activations and attention caches, so the actual peak usage is often higher than the weights alone.

When a model file exceeds the GPU's VRAM, the overflow portion falls back to system memory for processing, causing a significant drop in speed.

When deploying locally, VRAM cannot be filled to exact capacity; at least 1.5 to 2 GB of active VRAM must be reserved, otherwise long conversations will freeze or crash.

Using a comparably sized model to verify hallucinations doubles the VRAM requirements of the local environment.

The more frames extracted from a sprite sheet, the smoother the rotation transitions, but the more video memory the textures consume.