EXO: End-to-End Local AI Cluster Deployment Training Course

Introduction to EXO and Local AI Clustering

• Overview of the EXO framework and the exo-explore ecosystem
• Comparison between centralized cloud inference and distributed local inference
• Architecture details: libp2p device discovery, MLX backend, dashboard, and API layers
• Hardware requirements: Apple Silicon (M3 Ultra, M4 Pro/Max), Thunderbolt 5, and shared storage

Installing EXO on macOS

• Setting up Xcode, Metal ToolChain, and other macOS prerequisites
• Installing uv, Node.js, and the Rust nightly toolchain
• Installing the pinned macmon fork for Apple Silicon monitoring
• Cloning the repository and building the dashboard using npm
• Running EXO from source and verifying the localhost:52415 dashboard

Installing EXO on Linux

• Installing dependencies via apt or Homebrew on Linux systems
• Configuring uv, Node.js 18+, and Rust nightly
• Building the dashboard and running EXO in CPU-only mode
• Directory layout: XDG Base Directory paths for configuration, data, cache, and logs

Automatic Device Discovery and Cluster Formation

• Understanding libp2p-based auto-discovery across local networks
• Configuring custom namespaces using EXO_LIBP2P_NAMESPACE for cluster isolation
• Verifying node membership through the dashboard’s cluster view
• Addressing discovery failures and network segmentation issues

Enabling RDMA over Thunderbolt 5

• RDMA architecture and the claim of a 99% latency reduction
• Enabling RDMA via macOS Recovery mode using rdma_ctl
• Cable requirements and port topology constraints on Mac Studio devices
• Ensuring macOS version consistency across all cluster nodes
• Troubleshooting RDMA discovery and DHCP configuration

Deploying Frontier Models

• Using the dashboard to load and shard DeepSeek v3.1, Qwen3-235B, and Llama family models
• Previewing instance placements via the /instance/previews API endpoint
• Creating model instances utilizing pipeline or tensor-parallel sharding
• Configuring custom model cards sourced from the HuggingFace hub

Monitoring and Troubleshooting

• Interpreting EXO logs and understanding distributed tracing
• Analyzing cluster health within the dashboard’s cluster view
• Diagnosing worker node failures and observing reconnection behavior
• Leveraging EXO_TRACING_ENABLED for performance bottleneck analysis

Cluster Maintenance and Updates

• Updating EXO binaries and following dashboard rebuild procedures
• Migrating model caches and managing pre-downloaded models over NFS
• Gracefully removing nodes and rebalancing workloads