rlx-distributed

Multi-node distributed inference (pipeline / tensor parallel) for RLX models. It layers on rlx-driver's transports (TcpTransport, ThunderboltTransport, and rlx-mlx's MlxTransport) and ProcessGroup to run one model split across several hosts, coordinating per-rank layer blocks with a model-agnostic relay.

Modules

• config — hosts.json parsing + process-group construction ([DistConfig::connect]).
• partition — pipeline-parallel layer assignment ([pipeline_layer_range], [block_role]).
• pipeline — the model-agnostic relay ([PipelineCoordinator]) driving per-rank [BlockRunner]s.
• launch — local multi-process cluster helpers ([LocalCluster], [worker_args]).

A model family plugs in by implementing [BlockRunner] for its layer block.

Public API

use rlx_distributed::{DistConfig, ParallelMode, PipelineCoordinator, pipeline_layer_range};

let cfg = DistConfig::from_hostfile("hosts.json", /*rank*/ 0)?;
let group = cfg.connect()?;                     // ProcessGroup over the chosen transport
let (start, end) = pipeline_layer_range(rank, world, num_layers);
// build a BlockRunner for [start,end) and hand it to PipelineCoordinator
# anyhow::Ok(())

Quick start

cargo run -p rlx-distributed --example transport_bench

How it fits

Built on rlx-driver (ProcessGroup, transports). Model crates such as rlx-qwen3 provide the per-rank block runners.