Principle:Ggml org Llama cpp RPC Distributed Inference
| Knowledge Sources | |
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| Domains | Distributed, Networking |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
RPC Distributed Inference is the principle of distributing model inference across multiple machines using remote procedure calls.
Description
This principle covers the RPC (Remote Procedure Call) backend that enables llama.cpp to distribute tensor computations across multiple networked machines. An RPC server running on a remote machine exposes its compute resources (GPUs, CPUs) as a network-accessible backend. The client machine connects to one or more RPC servers and offloads layers or operations to them, enabling inference with models that exceed a single machine's memory or compute capacity.
Usage
Apply this principle when a model is too large to fit on a single machine's GPU memory, when you want to combine GPU resources from multiple machines, or when you need to separate the serving frontend from the compute backend.
Theoretical Basis
The RPC backend implements the GGML backend interface over a network protocol. Each RPC server advertises its available devices and supports operations including buffer allocation, tensor data transfer, and compute graph execution. The client-side RPC backend transparently proxies these operations over the network, making remote devices appear as local backends to the rest of the llama.cpp infrastructure. The protocol handles serialization of tensor metadata and data, synchronization of compute operations, and management of remote buffer lifetimes. This enables pipeline parallelism (different layers on different machines) and tensor parallelism (splitting operations across machines) depending on the configuration.