Edge provides an open-source library for developing and deploying efficient State Space Models (SSMs) on-device, targeting researchers and developers building real-time AI applications. It addresses the limitations of large, cloud-dependent models by offering optimized SSM architectures that achieve constant tokens per second and memory consumption, making them ideal for edge devices.

How It Works

Edge leverages State Space Models (SSMs), which offer a more computationally efficient alternative to Transformer architectures. The library focuses on custom, hardware-specialized inference kernels for SSMs like Mamba, enabling optimized performance across various accelerators. It also provides access to open-weight SSM models, pre-optimized for multiple hardware platforms, including CPU, CUDA GPUs, and Apple Silicon via Metal and MLX.

Quick Start & Requirements

• Install: pip install cartesia-pytorch or pip install cartesia-metal or pip install cartesia-mlx.
• Prerequisites: PyTorch, MLX, or Metal support depending on the package. Specific hardware requirements depend on the chosen backend (e.g., Apple Silicon for cartesia-metal).
• Resources: Model sizes range from 1B to 8B parameters.
• Docs: https://github.com/cartesia-ai/edge/tree/main/mlx (for MLX examples).

Highlighted Details

• Supports custom hardware-specialized inference kernels for SSM architectures.
• Offers open-weight SSM models (Rene-v0.1, Llamba family) optimized for PyTorch, MLX, and Metal.
• Includes distilled models (Llamba family) derived from Llama-3.2 and Llama-3.1.
• Provides quantization support for optimized on-device performance.

Maintenance & Community

• The project is actively developed by Cartesia AI.
• Contact information for custom support is available.

Licensing & Compatibility

• The specific license is not explicitly stated in the provided README snippet. Compatibility for commercial use or closed-source linking would require clarification of the license.

Limitations & Caveats

The README does not explicitly state the license, which is crucial for determining commercial use compatibility. While it supports multiple backends, the performance and feature set may vary across different hardware accelerators.