Multi GPU Local Inference: How to Drift a Model Across Your Home GPUs
Multi GPU local inference means spreading a single large language model across two or more GPUs in your home server so you can run models that exceed the VRAM of any one card, or serve them faster. You do this by “drifting” the model’s layers or tensors across your GPUs using either pipeline (layer) parallelism or tensor parallelism, configured through tools like llama.cpp and vLLM. The right strategy depends on your interconnect speed, your hardware mix, and whether your model architecture even supports the split. ...