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Course Outline
Introduction to EXO and Local AI Clustering
- Overview of the EXO framework and the exo-explore ecosystem.
- Comparing centralized cloud inference with distributed local inference.
- Architecture: 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, the Metal Toolchain, and 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 with npm.
- Running EXO from source and verifying the localhost:52415 dashboard.
Installing EXO on Linux
- Installing dependencies via apt or Homebrew on Linux.
- Configuring uv, Node.js 18+, and the Rust nightly toolchain.
- Building the dashboard and running EXO in CPU-only mode.
- Directory layout: XDG Base Directory paths for config, data, cache, and logs.
Automatic Device Discovery and Cluster Formation
- Understanding libp2p-based auto-discovery across local networks.
- Configuring custom namespaces with EXO_LIBP2P_NAMESPACE for cluster isolation.
- Verifying node membership in the dashboard cluster view.
- Handling discovery failures and network segmentation issues.
Enabling RDMA over Thunderbolt 5
- RDMA architecture and the claimed 99% latency reduction.
- Enabling RDMA in macOS Recovery mode using rdma_ctl.
- Cable requirements and port topology constraints on Mac Studio.
- Ensuring macOS versions match 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 with the /instance/previews API endpoint.
- Creating model instances with pipeline or tensor-parallel sharding.
- Configuring custom model cards from the HuggingFace hub.
Monitoring and Troubleshooting
- Reading EXO logs and understanding distributed tracing.
- Interpreting cluster health in the dashboard cluster view.
- Diagnosing worker node failures and reconnection behavior.
- Using EXO_TRACING_ENABLED for performance bottleneck analysis.
Cluster Maintenance and Updates
- Updating EXO binaries and procedures for rebuilding the dashboard.
- Migrating model caches and managing pre-downloaded models over NFS.
- Gracefully removing nodes and rebalancing workloads.
Requirements
- A solid understanding of networking fundamentals (IP addressing, subnetting, firewalls).
- Experience with command-line administration on macOS or Linux.
- Familiarity with Python package management (pip/uv) and Node.js tooling.
Target Audience
- System administrators.
- DevOps engineers.
- AI infrastructure architects responsible for on-premise LLM deployment.
21 Hours