Parallax by Gradient
Parallax is a fully decentralized inference engine that enables building distributed AI clusters for running large language models across multiple devices regardless of their configurations and physical locations.
https://github.com/GradientHQ/parallax?ref=producthunt
Product Information
Updated:Oct 31, 2025
What is Parallax by Gradient
Parallax, developed by Gradient, is an innovative open-source inference engine that reimagines model inference as a global, collaborative process. It breaks free from traditional centralized infrastructure by allowing large language models to be decomposed, executed, and verified across a distributed network of machines. The system supports cross-platform deployment on Windows, Linux, and macOS, with compatibility for various GPU architectures including Blackwell, Ampere, and Hopper series.
Key Features of Parallax by Gradient
Parallax is a fully decentralized inference engine that enables users to build their own AI cluster by distributing model inference across multiple nodes, regardless of their configuration or physical location. It offers cross-platform support, efficient model sharding through pipeline parallelism, and dynamic resource management capabilities, making it possible to run large language models on personal devices while maintaining high performance.
Distributed Model Inference: Allows model inference to be split and executed across multiple distributed nodes, enabling efficient use of available computing resources
Cross-Platform Compatibility: Supports multiple operating systems including Windows, Linux, and macOS, with flexible installation options through source code, Docker, or native applications
Dynamic Resource Management: Features dynamic KV cache management and continuous batching for Mac, along with intelligent request scheduling and routing for optimal performance
Pipeline Parallel Architecture: Implements pipeline parallel model sharding to efficiently distribute model layers across different nodes in the cluster
Use Cases of Parallax by Gradient
Personal AI Infrastructure: Individuals can run large language models on their personal devices by combining multiple computing resources
Distributed Research Environment: Research institutions can create collaborative AI environments by connecting multiple computers across different locations
Resource-Optimized Development: Developers can leverage existing hardware infrastructure by distributing model workloads across available devices
Pros
Enables running large language models on personal devices
Flexible deployment options across different platforms
Efficient resource utilization through distributed computing
Cons
Installation process can be lengthy (around 30 minutes)
Some features are platform-specific (e.g., certain Docker features limited to Linux+GPU)
How to Use Parallax by Gradient
Prerequisites Check: Ensure you have Python version 3.11.0 to 3.14.0 installed. For Blackwell GPUs, Ubuntu 24.04 is required.
Installation: Choose installation method based on your OS: Windows users can download installer, Linux/macOS users install from source, Linux GPU users can use Docker. For macOS, create Python virtual environment first.
Launch Scheduler: Start the scheduler on your main node by running 'parallax run'. Access the setup interface at http://localhost:3001. For non-frontend usage, use 'parallax run -m {model-name} -n {number-of-worker-nodes}'
Configure Cluster & Model: Through the web interface, select your desired node configuration and model from the supported list (including DeepSeek, MiniMax-M2, GLM-4.6, Kimi-K2, Qwen, gpt-oss, Meta Llama 3)
Connect Nodes: On each node you want to connect, run the join command: 'parallax join' for local network or 'parallax join -s {scheduler-address}' for public network
Start Using: Once nodes are connected, you can either use the web chat interface at http://localhost:3001 or make API calls to http://localhost:3001/v1/chat/completions for programmatic access
Optional Remote Access: To access chat interface from non-scheduler computers, run 'parallax chat' for local network or 'parallax chat -s {scheduler-address}' for public network, then visit http://localhost:3002
Uninstallation (if needed): For pip installation: use 'pip uninstall parallax'. For Docker: remove containers and images using docker commands. For Windows: uninstall through Control Panel
Parallax by Gradient FAQs
Parallax is a fully decentralized inference engine developed by Gradient that allows users to build their own AI cluster for model inference across distributed nodes, regardless of their configuration and physical location.
Parallax by Gradient Video
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