  Large Language Models (LLMs) based on Mixture-of-Experts (MoE) architecture
are showing promising performance on various tasks. However, running them on
resource-constrained settings, where GPU memory resources are not abundant, is
challenging due to huge model sizes. Existing systems that offload model
weights to CPU memory suffer from the significant overhead of frequently moving
data between CPU and GPU. In this paper, we propose Fiddler, a
resource-efficient inference engine with CPU-GPU orchestration for MoE models.
The key idea of Fiddler is to use the computation ability of the CPU to
minimize the data movement between the CPU and GPU. Our evaluation shows that
Fiddler can run the uncompressed Mixtral-8x7B model, which exceeds 90GB in
parameters, to generate over $3$ tokens per second on a single GPU with 24GB
memory, showing an order of magnitude improvement over existing methods. The
code of Fiddler is publicly available at
\url{https://github.com/efeslab/fiddler}
