Heterogeneous GPU Sharing on Kubernetes

KPilot: Unified control plane for multi-cluster Kubernetes management, GPU compute scheduling, and model serving.

TensorFusion landing page and product docs

The Ollama developer experience with the vLLM production power. Deploy local LLMs via Docker with smart and automatic GPU VRAM management.

🎤 Enhance speech-to-text with ultra-low-latency processing and smart GPU management for efficient, self-hosted solutions.

A smart GPU/node resource locking bot for teams — manage exclusive & shared access to cluster nodes and devices via chat commands or web UI.

🎙️ Generate high-quality speech from text with Llasa-TTS-8B, featuring intelligent GPU management and multi-language support for seamless integration.

🎙️ High-quality Text-to-Speech system based on Llasa-8B with intelligent GPU memory management. Features: 96% memory savings, Web UI + REST API + MCP, auto GPU selection, Docker deployment.

An intelligent NVIDIA GPU task scheduler with real-time monitoring and automatic job execution. Features persistent task queues, daemon scheduling, and multi-notification support for efficient GPU resource management.

A simple tool to expose only specified number of GPUs with desired memory to Tensorflow

ML Framework and CUDA Checker is a Python-based GUI application for checking PyTorch, TensorFlow, and CUDA installations. It provides detailed system specs, compatibility checks, advanced GPU management, and offers options to view instructions, export logs, and update machine learning frameworks.

Ultra-low-latency speech-to-text with intelligent GPU management - Enhanced version with lazy loading, auto resource release, and modern multi-language UI

a Vulkan-based Multi-Process Service (MPS)

Multi-server GPU slot management for LM Studio and other OpenAI-compatible inference backends

GPU management on multi-GPU platforms