How AI Models Scale Beyond a Single GPU Across LLM Workloads

IBM Technology's short explainer, presented by Grace Ableidinger, is about serving a large language model that is too big for one GPU. It runs about nine minutes and moves through the main ways to split inference work, one chapter each.

The opening chapter at 0:00 sets up the problem. The biggest models do not fit in a single GPU's memory, so serving them calls for distributed inference. At 1:50 the video turns to data parallelism as the way to scale for more LLM traffic. Pipeline parallelism follows at 2:45, splitting a model across GPUs. At 3:45 tensor parallelism divides the model's layers between devices.

Mixture-of-experts models get their own chapter at 4:32, on spreading experts over several GPUs (the description calls this expert parallelism). From 5:44 the focus shifts from model layout to workload: prefill and decode are separate phases of LLM serving, and the video treats them apart. This is where the description places KV cache, throughput and prefill/decode bottlenecks, the factors it says shape production serving.

The last two chapters pull the pieces together. At 7:51 the video covers combining GPU parallelism methods for production AI, and the final chapter at 8:29 is about orchestrating distributed LLM inference.

The description names no specific serving framework or hardware model, so any tools shown on screen are unknown from the sources available here. It also states that AI was used in creating the video's transcript and metadata.