NEO: Saving GPU Memory Crisis with CPU Offloading for Online LLM Inference

Online LLM inference powers many exciting applications such as intelligent chatbots and autonomous agents.Modern LLM inference engines widely rely on request batching to improve inference throughput, aiming to makeit cost-efficient when running on expensive GPU accelerators. However, the limited GPU memory has largelylimited the batch size achieved in practice, leaving significant GPU compute resources wasted.We present NEO, an online LLM inference system that offloads part of attention compute and KV cache statesfrom the GPU to the local host CPU, effectively increasing the GPU batch size and thus inference throughput. Tothis end, NEO proposes asymmetric GPU-CPU pipelining and load-aware scheduling to balance GPU and CPUloads and fully utilize their compute and memory resources. We evaluate NEO on a wide range of workloads (i.e.,code generation, text summarization), GPUs (i.e., T4, A10G, H100), and LLM models (i.e., 7B, 8B, 70B). NEOachieves up to 7.5×, 26%, and 14% higher throughput compared to GPU-only approach on T4, A10G, and H100GPUs, respectively, while maintaining the same latency; with more powerful CPUs, NEO achieves up to 79.3%throughput gain on A10G GPU. To facilitate future research, we open-source our code at https://github.com/NEO-MLSys25/NEO.