bigscience-workshop/petals

Features

• Distributed Inference Engines - Provides a framework for splitting and executing massive language model inference across a collaborative network of GPUs.
• Model Sharding - Splits neural network layers across multiple network nodes to execute models larger than any single machine's memory.
• Collaborative GPU Swarms - Establishes a decentralized network of connected devices that collectively host model weights and execute inference.
• Dynamic GPU Block Allocation - Allocates model layers to GPUs based on VRAM and adjusts hosting patterns to optimize swarm throughput.
• Collaborative GPU Sharing - Enables sharing local GPU resources to serve specific parts of a model and increase collective network capacity.
• Large Language Model Serving - Implements strategies for hosting and serving models that exceed the memory capacity of any single machine.
• Model Inference Servers - Provides a distributed inference engine that splits large language model computations across multiple network nodes.
• Weight Distribution - Implements strategies for splitting and hosting model parameters across multiple GPU devices in a distributed swarm.
• Pipeline Parallelism Partitioners - Implements pipeline-parallelism by partitioning large neural networks into sequential layers across multiple remote GPUs.
• Multi-GPU Layer Distribution - Spreads individual model layers across a collaborative network of computers to execute models exceeding single-device memory.
• ML Model Hosting - Supports loading and serving model weights directly from remote repositories without requiring local format conversion.
• Peer-to-Peer Networking - Implements a peer-to-peer networking model where nodes collectively host and serve large model weights.
• Peer Discovery - Utilizes distributed hash tables and bootstrap peers to discover and connect available compute nodes across the internet.
• Distributed Prompt Tuning - Adapts massive models to specific tasks using prompt-tuning techniques leveraging shared network resources.
• Large Language Model Fine-Tuning Frameworks - Provides a platform for adapting large models to specific tasks via distributed prompt-tuning.
• Distributed Fine-Tuning - Enables updating model behavior for specific tasks through distributed prompt-tuning on a network.
• Private AI Deployments - Enables the creation of private distributed compute networks to ensure data privacy and restricted access.
• Remote Weight Streaming - Loads model parameters directly from remote repositories into GPU memory to eliminate local file conversion steps.
• Trusted Host Establishment - Enables establishing restricted swarms of trusted hosts to ensure data is processed only by authorized organizations.
• Private Networks - Supports launching restricted networks of compute nodes for private model inference and fine-tuning.
• Circuit Relay Hosting - Implements circuit relaying to route traffic between peers, bypassing firewalls and NAT for nodes without public IPs.
• Peer Connectivity - Uses bootstrap peers and relays to establish and maintain stable peer-to-peer connectivity across firewalls.
• Private Data Processing Environments - Enables the creation of restricted networks of trusted hardware to process sensitive data in isolation.
• Private Network Security - Allows for the creation of restricted, trusted networks to process sensitive data without public swarm exposure.
• GPU Block Memory Management - Allows controlling which model blocks a server hosts and how many to load based on available GPU memory.
• Chat Interfaces - Distributed inference and fine-tuning using peer-to-peer resources.
• Inference Frameworks - Distributed inference and fine-tuning over internet-connected nodes.
• Large Language Models - Distributed inference and fine-tuning for massive models.
• Developer Tools and Infrastructure - Distributed platform for running large AI models.
• Large Language Models (LLMs) - Listed in the “Large Language Models (LLMs)” section of the The Incredible Pytorch awesome list.

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