How AI Models Scale Across Multiple GPUs for Large Language Model Workloads

Large AI models often exceed the memory and compute capacity of a single GPU, requiring distributed inference approaches to serve them at scale. This IBM video breaks down the main parallelism strategies: data parallelism spreads traffic across GPUs, pipeline parallelism splits model layers across devices, tensor parallelism divides individual layers, and mixture-of-experts parallelism distributes specialized model components.

The video also covers production bottlenecks in LLM serving: KV cache memory constraints, prefill and decode workload imbalances, and throughput limits. The final segments show how these techniques combine in real systems and how orchestration tools manage distributed inference clusters.

Understanding these scaling methods matters for anyone building or deploying large language models at scale, from infrastructure engineers to AI teams running production systems.