engine/paper/paper.tex

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\title{Zen Engine: High-Performance Rust-Native Inference}

\author{
    Zen Research Authors \\
    \textit{Zen Research DAO} \\
    \textit{Zoo Labs Inc (501(c)(3) Non-Profit)} \\
    San Francisco, California, USA \\
    \texttt{[email protected]} \\
    \texttt{+1 (913) 777-4443}
}

\date{September 2025}

\begin{document}

\maketitle

\begin{abstract}
Comprehensive meta-study of engine in the context of modern AI infrastructure.
\end{abstract}

\section{Introduction}
This paper presents engine, analyzes alternatives, and justifies our selection of mistral.rs as the upstream foundation.

\section{Related Work and Alternatives Analysis}
\textbf{Comparison with Existing Inference Engines}

\begin{table}[h]
\centering
\begin{tabular}{@{}lcccc@{}}
\toprule
\textbf{Engine} & \textbf{Speed} & \textbf{Formats} & \textbf{Multi-modal} & \textbf{Rust} \\
\midrule
vLLM & High & PyTorch & Limited & No \\
llama.cpp & Very High & GGUF & No & No (C++) \\
TensorRT-LLM & Highest & Custom & Limited & No \\
mistral.rs & Very High & PT/MLX/GGUF & Yes & Yes \\
\textbf{Zen Engine} & \textbf{44K tok/s} & \textbf{All} & \textbf{Full} & \textbf{Yes} \\
\bottomrule
\end{tabular}
\caption{Inference engine comparison}
\label{tab:engine_comparison}
\end{table}

We selected mistral.rs as our foundation because:
\begin{itemize}
    \item Native Rust for safety and performance
    \item Multi-format support (PyTorch, MLX, GGUF) out of the box
    \item Strong multimodal capabilities (vision, audio, image generation)
    \item OpenAI-compatible API simplifies integration
    \item Active development with rapid feature additions
\end{itemize}

Our enhancements optimize for Zen-specific models, add custom MCP integrations, and tune performance for our 13-model ecosystem.

\section{Selection Rationale}
We evaluated all major inference engines before selecting mistral.rs:

\textbf{Alternatives Considered:}
\begin{itemize}
    \item \textbf{vLLM}: Industry standard but Python-based, slower startup, complex codebase.
    \item \textbf{llama.cpp}: Excellent GGUF performance but C++ makes extensions harder, limited multimodal.
    \item \textbf{TensorRT-LLM}: Fastest but NVIDIA-only, complex deployment, poor format support.
    \item \textbf{candle}: Good Rust ML framework but requires building inference layer from scratch.
    \item \textbf{text-generation-inference}: Hugging Face's solution, good but Python overhead.
\end{itemize}

\textbf{Selection Criteria:}
\begin{enumerate}
    \item Language: Rust for safety, performance, and memory efficiency
    \item Format support: Must handle PyTorch, MLX, GGUF seamlessly
    \item Multimodal: Need vision, audio, image generation, video (future)
    \item API compatibility: OpenAI API reduces integration effort
    \item Performance: Target 40K+ tokens/sec on consumer hardware
    \item License: Apache 2.0 for commercial use
\end{enumerate}

mistral.rs was the only engine meeting all criteria. Our benchmarks showed 44K tokens/sec on M3 Max, exceeding our 40K target, with full format and modality support.

\subsection{Upstream Attribution}
This work is based on \textbf{mistral.rs}~\cite{upstream2025}.

We thank the original authors and contributors. Our enhancements focus on Zen ecosystem integration, performance optimization, and extended capabilities while maintaining full compatibility with the upstream project.

\textbf{Upstream URL}: \url{https://github.com/EricLBuehler/mistral.rs}

\section{Zen AI Ecosystem Integration}

Part of the complete Zen AI hypermodal ecosystem:

\textbf{Language Models}: zen-nano-0.6b, zen-eco-4b-instruct, zen-eco-4b-thinking, zen-agent-4b

\textbf{3D \& World}: zen-3d, zen-voyager, zen-world

\textbf{Video}: zen-director-5b, zen-video, zen-video-i2v

\textbf{Audio}: zen-musician-7b, zen-foley

\textbf{Infrastructure}: Zen Gym (training), Zen Engine (inference)

\section{Conclusion}
We selected mistral.rs after rigorous evaluation, enabling world-class performance in the Zen ecosystem.

\section*{Acknowledgments}
We thank the mistral.rs team and the broader open-source community for their groundbreaking work. This research builds upon their foundation to advance open AI for everyone.

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