Apple M3 Ultra: AI Inference King? |NVIDIA SOCAMM| Project Digits | Low-Latency AI with Batch Size 1 скачать в хорошем качестве

Apple M3 Ultra: AI Inference King? |NVIDIA SOCAMM| Project Digits | Low-Latency AI with Batch Size 1

The M3 Ultra has been released, supporting up to 512GB of memory, making a single Mac Studio powerful enough to run DeepSeek for FP4 inference—a feat that even NVIDIA’s H100 or Blackwell GPUs cannot achieve on their own. Despite being a consumer-grade machine with lower bandwidth and TFLOPS compared to high-end NVIDIA server GPUs, the Mac Studio has become irreplaceable for batch size = 1 inference workloads. To fully understand this, we explore large language model (LLM) inference and optimization techniques, explaining the difference between batch size = 1 inference and processing multiple sequences simultaneously in a simple way. We focus on how model parameters are loaded into GPU memory and how floating-point computation bottlenecks occur. As batch size increases, the same parameters are computed across multiple sequences, improving overall efficiency by spreading memory transfer costs across multiple computations—a key factor in achieving optimal performance. This comparison highlights the key differences between NVIDIA GPUs and Apple Silicon. Additionally, we examine how Apple Silicon’s unified memory architecture and high memory-to-FLOPS ratio provide advantages in LLM inference. Written by Error Edited by Jin-Yi Lee [email protected]