diffusers-rs
High-performance, memory-safe Stable Diffusion inference using Rust and Libtorch.

The open-standard inference engine for high-performance multi-model serving.
MLServer is a highly optimized, open-source inference server designed to serve machine learning models through a standardized V2 Inference Protocol. Developed primarily by Seldon, it serves as the core engine for Seldon Core v2 and is a key component in the KServe ecosystem. By 2026, MLServer has solidified its position as the industry standard for Python-based inference due to its ability to wrap multiple frameworks—including Scikit-Learn, XGBoost, LightGBM, and MLflow—within a unified, high-performance interface. Its architecture leverages multi-process parallelism to bypass the Python Global Interpreter Lock (GIL), making it suitable for high-throughput production environments. The engine supports both HTTP and gRPC interfaces, adaptive batching, and custom runtimes, allowing data scientists to deploy complex logic without managing the underlying networking stack. As organizations move toward standardized MLOps pipelines, MLServer’s compatibility with NVIDIA Triton and its native integration with Prometheus for observability make it an essential tool for scalable, enterprise-grade AI deployment.
Implements the KFServing V2 dataplane, ensuring compatibility across different inference servers like Triton.
High-performance, memory-safe Stable Diffusion inference using Rust and Libtorch.
Scalable cloud-native infrastructure for high-performance computer vision data management and annotation.
The industry-standard library for high-performance, multi-modal data loading and preprocessing in Python.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Dynamically groups individual inference requests into larger batches before processing.
Allows developers to write custom Python classes to handle pre-processing and post-processing logic.
Capable of loading and serving multiple models from a single server instance.
Utilizes a pool of worker processes to handle requests, bypassing the Python GIL.
Native runtime that can directly load and serve models saved in the MLflow format.
Exposes /metrics endpoint for tracking latency, request count, and model errors.
An organization needs to serve a Scikit-learn model and an XGBoost model from the same infrastructure.
Registry Updated:2/7/2026
A recommendation engine receives thousands of requests per second and needs efficient processing.
Input data needs normalization and feature engineering before reaching the model.