Prefill and Decode Want Different Chips. The Economics Finally Agree.

The analysis by Gimlet Labs explores the efficiency gains of using different hardware vendors for distinct phases of large language model (LLM) inference, specifically highlighting a B200:Gaudi 3 setup for prefill-heavy and decode-heavy workloads. By employing separate hardware vendors for prefill and decode processes—where prefill is compute-bound and decode is memory-bandwidth-bound—a significant total cost of ownership (TCO) benefit is achieved compared to traditional homogeneous configurations like all-NVIDIA setups. This approach leverages the strengths of specific chips, such as NVIDIA B200's compute capacity for prefill and Intel Gaudi 3's memory bandwidth for decode, resulting in TCO improvements of up to 4x. The findings suggest that heterogeneous inference setups could reshape the economics of AI serving, emphasizing the importance of a unified serving layer that efficiently manages cross-vendor deployments. However, the current challenge lies in software support for multivendor deployments, as existing tools like vLLM and llm-d show uneven support, making it difficult to implement these configurations in production. The development of cross-vendor schedulers that manage KV-cache data movement and dynamic partitioning is identified as a crucial infrastructure problem for advancing AI inference technology.