# mw00/project-maya

> Project Maya - run GLM-5.3-Flash (321B MoE) and DeepSeek-V4.1-Flash-1017 (748B MoE) privately on your own GPU(s), from one card to 16, NVIDIA or AMD, Linux or Windows. Engine, server, dashboard, OpenAI- and Anthropic-compatible API, quant tools.

## Fast Facts & Telemetry
- **Canonical URL**: https://githubrepo.cloud/repo/mw00/project-maya
- **GitHub Source**: https://github.com/mw00/project-maya
- **Global Rank**: #6,115 on GitHubRepo
- **Stars**: 1,137
- **Forks**: 145
- **Open Issues**: 67
- **Primary Language**: C++
- **Software License**: MIT
- **Last Commit / Push**: Oct 20, 2026
- **Topics**: cuda, deepseek, deepseek-v4, glm, glm-5, llm, llm-inference, local-llm, mixture-of-experts, moe, openai-api, quantization, rocm, self-hosted

## Project Overview
project-maya is an open-source project written in C++: Project Maya - run GLM-5.3-Flash (321B MoE) privately on your own GPU(s), from one card to 16, NVIDIA or AMD, Linux or Windows. Engine, server, dashboard, OpenAI- and Anthropic-compatible API, quant tools.

Engineered for speed, consistency, and developer ease, it solves common hurdles in cuda, glm, glm-5. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.

## Key Features
- Optimized execution pipeline written in C++ for predictable speed.
- Zero-friction configuration with comprehensive sensible defaults out of the box.
- Cross-platform runtime support across Linux, macOS, and Windows environments.
- Strong typing and modular architecture designed for easy extension and maintainability.
- Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
- Active community maintenance with regular dependency updates and security patches.

## Installation & Quick Start
### Linux
```bash
git clone https://github.com/mw00/project-maya.git
cd project-maya
```

### macOS
```bash
git clone https://github.com/mw00/project-maya.git
cd project-maya
```

### Windows
```bash
git clone https://github.com/mw00/project-maya.git
cd project-maya
```

## System Requirements & Architecture
- **Supported Platforms**: macOS, Linux, Windows
- **Runtime Dependencies**: C++20 compliant compiler (GCC 11+, Clang 13+, MSVC)
- **Architecture**: x86_64, ARM64 (Apple Silicon & Graviton)

## How It Works
project-maya coordinates its core functionality through a modular C++ pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.

## Production Use Cases
### Autonomous AI Agents
Orchestrate intelligent workflows and tool-calling routines with project-maya.

### Model Inference & Prompting
Integrate fast, local or cloud-hosted generative AI models directly into production code.

### Context Memory & RAG
Augment language models with dynamic vector retrieval and structured project memory.

### Developer Productivity
Automate repetitive engineering tasks, code generation, and test creation using AI agents.

## Target Audience
Developers and engineering teams building with C++, seeking reliable, tested, and actively maintained tooling for production workloads.

## Strengths
- Active community backing with 860 GitHub stars and verified adoption.
- Permissive open-source distribution under the MIT license.
- Built in C++ for high execution speed and developer familiarity.
- Cross-platform compatibility across modern Linux, macOS, and Windows environments.
- Clean modular design allowing flexible configuration and pipeline integration.

## Considerations
- Requires familiarity with C++ and modern CLI workflows.
- Ecosystem extensions may require manual configuration depending on environment constraints.
- Active development roadmap means breaking API changes may occur across major versions.

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