Plain-English explanation
If a model and its working data exceed available VRAM, an application does not always have to fail. It can keep selected layers in system RAM, move them to the GPU before use, and return them afterward. More aggressive schemes store weights on disk until needed. This expands effective capacity but introduces traffic across much slower links.
Offloading is a placement strategy, not compression. Quantization reduces the size or precision of data; offloading changes where that data resides. They are often used together.
Common strategies
- Layer or device-map placement: keep some layers on the GPU and others on CPU or another accelerator.
- CPU offload: transfer a layer to the execution device only for its forward pass.
- Disk offload: memory-map weights from storage when RAM is also constrained.
- Cache offload: move the language model's KV cache or older cache layers away from the GPU.
Frameworks such as Hugging Face Accelerate provide hooks and automatic device maps, while end-user tools expose simplified layer counts or low-memory modes.
Where it appears in local AI
KoboldCpp can assign part of a quantized language model to a GPU, leaving the remainder for CPU execution. Diffusion workflows in ComfyUI may load and unload text encoders, denoisers, and VAEs around each stage. The practical benefit depends on model order, transfer size, storage speed, and whether the workload repeatedly swaps the same components.
Offloading versus distributed inference
Offloading primarily addresses memory placement and may involve one GPU plus CPU or disk. Distributed inference divides computation across multiple accelerators or machines for capacity or throughput. A device map can participate in either pattern, but offloading to slow storage has a very different performance profile from splitting layers across fast GPUs.
Tradeoffs
- PCIe transfers and disk reads can dominate latency.
- System RAM requirements may approach or exceed the uncompressed model size.
- Automatic placement is convenient but not necessarily optimal for every architecture.
- Offloading can enable a workload; it does not make inadequate hardware equivalent to an all-GPU setup.
Technical context
The local diffusion dossier distinguishes component, sequential and group offloading inside complete pipelines.