RDMA Point-to-Point Communication for LLM Systems
Authors: name: Nandor Licker, name: Kevin Hu, name: Vladimir Zaytsev, name: Lequn Chen
Organization: Perplexity

Emerging Large Language Model (LLM) system patterns, such as disaggregated
inference, Mixture-of-Experts (MoE) routing, and asynchronous reinforcement
fine-tuning, require flexible point-to-point communication beyond simple
collectives. Existing implementations are locked to specific Network Interface
Controllers (NICs), hindering integration into inference engines and
portability across hardware providers. We present TransferEngine, which bridges
the functionality of common NICs to expose a uniform interface. TransferEngine
exposes one-sided WriteImm operations with a ImmCounter primitive for
completion notification, without ordering assumptions of network transport,
transparently managing multiple NICs per GPU. We demonstrate peak throughput of
400 Gbps on both NVIDIA ConnectX-7 and AWS Elastic Fabric Adapter (EFA). We
showcase TransferEngine through three production systems: (1) KvCache transfer
for disaggregated inference with dynamic scaling, (2) RL weight updates
achieving 1.3 seconds for trillion-parameter models, and (3) MoE
dispatch/combine implementation exceeding DeepEP decode latency on ConnectX-7,
with the first viable latencies on EFA. We demonstrate that our portable
point-to-point communication complements collectives while avoiding lock-in.