A scalable inference server for models optimized with OpenVINO™
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Updated
May 31, 2024 - C++
A scalable inference server for models optimized with OpenVINO™
The easiest way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Multi-model Inference Graph/Pipelines, LLM/RAG apps, and more!
A high-throughput and memory-efficient inference and serving engine for LLMs
⛅ Versatile Data Pipeline (VDP) console website
Multi-LoRA inference server that scales to 1000s of fine-tuned LLMs
🏡 Instill AI organisation profile and default configuration
The simplest way to serve AI/ML models in production
Standardized Serverless ML Inference Platform on Kubernetes
MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.
PyTorch/XLA integration with JetStream (https://github.com/google/JetStream) for LLM inference"
Tools for easing the handoff between AI/ML and App/SRE teams.
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, TensorOpera AI (https://TensorOpera.ai) is your generative AI platform at scale.
OneDiffusion: Run any Stable Diffusion models and fine-tuned weights with ease
LightLLM is a Python-based LLM (Large Language Model) inference and serving framework, notable for its lightweight design, easy scalability, and high-speed performance.
JetStream is a throughput and memory optimized engine for LLM inference on XLA devices, starting with TPUs (and GPUs in future -- PRs welcome).
Okik is a command-line interface (CLI) tool for LLM, RAG and model serving.
Hopsworks - Data-Intensive AI platform with a Feature Store
AICI: Prompts as (Wasm) Programs
🏕️ Reproducible development environment
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