Tensor Parallelism: Split Layers, Not Just Models
Tensor Parallelism splits a single large model layer, like a weight matrix, across multiple GPUs to run in parallel. This is crucial for inference with models whose layers exceed a single GPU's VRAM.
Tensor Parallelism splits a single, massive calculation across a team of GPUs. Instead of one GPU handling a huge weight matrix, you slice the matrix column-wise, let each GPU compute its part of the multiplication, then concatenate the results. This is essential for serving LLMs whose individual layers exceed a single GPU's VRAM. The critical footgun is assuming it's a universal feature; it requires explicit support for the model's architecture and won't work for arbitrary models dropped into a serving framework.
Read the original → huggingface.co
- #llms
- #gpu
- #distributed systems
- #model serving
Get five bites like this every day.
Tezvyn delivers a daily feed of 60-second tech bites with quizzes to lock in what you learn.