VRAM is the fast memory attached to a GPU. The model's weights and its temporary computations must fit inside it; exceed the limit and the model will not load. Quantization shrinks weights specifically to fit larger models into smaller VRAM.
The book · Volume 7 of 7
AI for Video: A Complete Guide to Intelligence in Video Products: From Real-Time Pipelines and Agents to Serving, Cost, Compliance, and Verticals
VRAM is usually the first hard limit a video AI team hits: the model does not fit, or it fits but batching does not. Volume 7 covers VRAM in detail within serving and infrastructure: GPU sizing, quantization and distillation for smaller memory, batching in vLLM and Triton, and the cost per stream-hour. Written by Nikolay Sapunov, CEO at Fora Soft.
Your model does not fit in VRAM on the GPUs you can afford?
Send us the model and target hardware, and we will propose quantization or serving changes that fit VRAM and estimate the quality trade-off. 250+ video products shipped since 2005.
