GPU memory limits what AI can run
VRAM (video RAM) is dedicated GPU memory that holds model weights, activations, and batch data during training and inference. Running out of VRAM stops jobs, forces smaller batches, or requires model sharding across multiple GPUs.
What happens when VRAM is exhausted
- Training or inference jobs fail with out-of-memory errors
- Batch sizes shrink, slowing throughput
- Model sharding or multi-GPU setups become necessary
- Operators may need different hardware generations with more memory
Why VRAM affects hardware decisions
Larger language models and higher-resolution vision models need more memory. VRAM capacity often determines which workloads a GPU can run without expensive workarounds.
Frequently Asked Questions
What is VRAM?
VRAM is the dedicated memory on a GPU used to hold model data and intermediate calculations during AI workloads.
What happens when you run out of VRAM?
Jobs may fail, slow down, or require smaller batches, model sharding, or different hardware.
Why is VRAM important for AI?
Larger models and batches need more memory. VRAM often determines what workloads a GPU can run.