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 fabric-lib, which bridges the functionality of common NICs to expose a uniform interface. fabric-lib 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 fabric-lib 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. fabric-lib is open-sourced at https://github.com/perplexityai/pplx-garden/
@article{arxiv.2510.27656,
title = {fabric-lib: RDMA Point-to-Point Communication for LLM Systems},
author = {Nandor Licker and Kevin Hu and Vladimir Zaytsev and Lequn Chen},
journal= {arXiv preprint arXiv:2510.27656},
year = {2026}
}