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BentoML is a machine learning model serving framework and GPU-accelerated inference server designed to package, deploy, and scale AI models as production-ready REST APIs. It functions as an AI model lifecycle manager and an inference graph orchestrator, enabling the chaining of multiple models and custom logic into complex pipelines for advanced task sequences. The framework distinguishes itself through a dynamic batching engine that optimizes GPU throughput and an artifact-based packaging system that bundles model weights and dependencies into immutable archives for consistent deployment. It
Jina is a cloud-native framework for building and deploying multimodal AI applications that process text, images, and audio across distributed microservices. It functions as an inference orchestrator and a distributed model gateway, providing a containerized stack to organize AI executors into operational pipelines. The system manages large language model workloads through token-streamed response delivery and dynamic batching to increase hardware throughput. It utilizes a protocol-agnostic communication layer to route data across different machine learning frameworks. The framework covers hi
⚡ Langchain apps in production using Jina & FastAPI
RayLLM - LLMs on Ray (Archived). Read README for more info.
The main features of ray-project/ray-llm are: Serving Frameworks.
Projects with overlapping indexed features include: ajndkr/lanarky — The web framework for building LLM microservices [deprecated]. bentoml/bentoml — BentoML is a machine learning model serving framework and GPU-accelerated inference server designed to package,… jina-ai/jina — Jina is a cloud-native framework for building and deploying multimodal AI applications that process text, images, and… jina-ai/langchain-serve — ⚡ Langchain apps in production using Jina & FastAPI. kaito-project/kaito — Kubernetes AI Toolchain Operator. mosecorg/mosec — A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your…
