#Distributed Systems (8)
- Distributed LLMs: Why P2P Networks Matter
This chapter explores large language model challenges, detailing how distributed computing and peer-to-peer networks address scale and resource demands, preparing for Mesh LLM and iroh.
- Iroh Primitives: How Data and Peers Connect
This chapter introduces Iroh's core concepts—blobs, docs, tickets, and peer discovery—to manage and share data efficiently, laying the foundation for distributed LLM architectures.
- Mesh LLM Core Concepts: Parallelism and Sharding
This chapter introduces the essential techniques of tensor parallelism, pipeline parallelism, and model sharding that enable large language models to operate across distributed hardware.
- Iroh & Mesh LLM: Distributed Architecture Patterns
This chapter explores Iroh's data synchronization and peer-to-peer networking integration with Mesh LLM's distributed computing model, enabling robust and scalable LLM architectures.
- Mesh LLM Node: Local Setup Walkthrough
This chapter guides you through configuring and running a single Mesh LLM inference node on your local machine, detailing its essential components and initial setup for distributed operations.
- Distributed LLM Inference: Multi-Node System Assembly
Assemble a multi-node distributed LLM inference system, orchestrating model sharding and parallel execution across Iroh peers, building upon established concepts for a functional prototype.
- Iroh Peer Connectivity: How Mesh LLMs Share Data
This chapter details how Iroh enables secure peer-to-peer connections and efficient data exchange, crucial for distributing LLM weights and intermediate activations across a Mesh LLM network.
- Mesh LLM on iroh: Distributed LLM Architecture
Master distributed LLM architectures using Mesh LLM and iroh, guiding engineers from foundational concepts to deploying scalable, peer-to-peer LLM inference systems.