xorbitsai/inference
Inference
This project is a platform for the deployment of open source large language and multimodal models. It provides a unified interface to serve text, image, and speech models across local or cloud hardware.
The system enables distributed AI inference by orchestrating model workloads across multiple nodes and devices. It includes a unified API adapter layer to standardize inputs and outputs, as well as tools for multimodal chat and structural image generation.
The platform covers a broad capability surface including request batching for throughput optimization, dynamic model loading, and integration with autonomous agent frameworks through tool-based function calling. It also provides performance benchmarking tools to measure latency and throughput across varying context lengths.
Deployment is supported via Helm charts for automated configuration within containerized cluster environments.
Features
- Model Serving Interfaces - Provides a unified interface to serve text, image, and speech models across local or cloud hardware.
- Open Source Models - Provides a platform for deploying and running open source large language and multimodal models on local or cloud hardware.
- OpenAI-Compatible APIs - Exposes standard HTTP endpoints that implement the OpenAI API specification for seamless integration into existing AI ecosystems.
- Hardware-Accelerated Inference - Distributes inference tasks across available GPUs and CPUs to accelerate processing speeds based on system capacity.
- Model Serving - Deploys trained machine learning models as production-ready inference endpoints via REST APIs.
- Multimodal Inference - Serves a variety of text, image, and speech models within a single multimodal production environment.
- Distributed Inference Clusters - Orchestrates model workloads across multiple network nodes and GPU clusters to scale inference capacity.
- Request Batching - Implements request batching to group concurrent incoming inference requests and increase hardware throughput.
- Unified Model Wrappers - Standardizes diverse model inputs and outputs through a unified interface to enable seamless model swapping.
- Agent Framework Integrations - Provides adapters to connect inference services to autonomous reasoning platforms for multi-step task execution.
- AI Agent Integrations - Connects language models to external tool-calling and reasoning frameworks for autonomous agentic workflows.
- Inference Scaling Tests - Includes benchmarking tools to measure how inference performance scales as input context size increases.
- Performance Benchmarks - Measures model latency and throughput across varying context lengths to evaluate hardware efficiency.
- Model Loading - Optimizes hardware utilization by loading and offloading model weights into memory on demand.
- Hosting Architectures - Supports a wide variety of model architectures, including text-to-image, audio, and text embedding models.
- Function Calling Interfaces - Implements interfaces that allow models to trigger programmatic external functions based on user requests.
- Multimodal Conversational Interfaces - Processes combined text and visual inputs to enable conversational interactions about images.
- Speech to Text Transcription - Processes audio input through speech-to-text models to produce written transcriptions.
- Structural Image Generation - Generates images using structural guidance to maintain precise visual layouts.
- Inference Batching - Groups concurrent inference requests into batches to maximize hardware throughput and reduce latency.
- Inference Performance Monitoring - Ships tools to evaluate model latency and throughput by running requests against datasets.
- Artificial Intelligence - Platform for running multimodal and LLM models with simple integration.
- Inference and Serving - Library for serving language and multimodal models.
- Inference Engines - Library for serving language and multimodal models.
- Serving Frameworks - Drop-in replacement for OpenAI API with multi-model support.
