Modern AI workloads have moved well beyond single-GPU inference serving. Model parallelism, which efficiently splits computation across many GPUs, is now the…
Modern AI workloads have moved well beyond single-GPU inference serving. Model parallelism, which efficiently splits computation across many GPUs, is now the foundation of scalable, state-of-the-art deployments. The highest-performing models increasingly adopt mixture-of-experts (MoE) architectures, which are more efficient than dense models because they activate only a subset of trained…
