What is GPU memory and why does it matter?
GPU memory (VRAM) stores textures, shaders, framebuffers, and processed data during rendering. More VRAM = more data the GPU can hold = higher resolutions, higher quality textures, larger batches of AI processing. Running out of VRAM = GPU spills data to system RAM (much slower) = FPS drops by 30–50%. VRAM types: GDDR6 (gaming, cheaper, slower), GDDR6X (slightly faster), HBM2 (AI workstations, expensive), GDDR5 (older cards, slower).
Gaming: VRAM requirements by resolution
1080p high settings: 6–8GB sufficient (most games use 4–6GB) 1440p ultra settings: 8–10GB ideal (newer AAA games use 7–9GB) 4K ultra settings: 12GB+ recommended (Cyberpunk, Starfield use 9–11GB) Example: RTX 4070 (12GB) at 1440p handles any game. RTX 4060 (8GB) at 1440p struggles in bandwidth-heavy games (Alan Wake 2, Unreal Engine 5 demos).
Video editing: VRAM for 4K and 8K workflows
4K H.264/H.265 editing: 8GB is minimum, 12GB ideal (Premiere Pro, DaVinci Resolve). 4K ProRes or DCI 4K: 12GB minimum. 8K or higher: 20GB+ (RTX 6000 Ada, professional cards). Note: VRAM matters less than GPU compute performance for editing (which determines export speed). But insufficient VRAM causes playback stuttering.
AI and machine learning
Fine-tuning LLMs: 24GB+ (A6000, RTX 6000 Ada, H100) Inference (running models): 8–12GB (RTX 4070, RTX 4090) Stable Diffusion, local LLMs: 8GB+ (RTX 3080, RTX 4070) Consumer GPUs hit a wall at 12–16GB VRAM. Professional cards (RTX 6000 Ada, H100) with 48–80GB are expensive but necessary for large model training.