How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
Kubernetes operator for local LLM inference with llama.cpp, vLLM, TGI, and mlx-server — multi-GPU NVIDIA + Apple Silicon Metal, autoscaling, air-gapped, production-ready
The main features of defilantech/llmkube are: Model Serving Engines.
Projects with overlapping indexed features include: deep-diver/alpaca-lora-serve — LLM as a Chatbot Service. flowiseai/flowise — Flowise is a low-code platform designed for building and deploying complex language model workflows through a visual,… fminference/flexgen — FlexGen is an inference engine for large language models designed for high-throughput execution on single or multiple… fujitsuresearch/onecompression — Python package for LLM compression. ggerganov/llama.cpp — llama.cpp is a high-performance C++ inference engine and runtime for executing large language models locally across… alichherawalla/off-grid-mobile-ai — The Swiss Army Knife of Offline AI. Chat, Speak, and Generate Images - Privacy First, Zero Internet. Download an LLM…
LLM as a Chatbot Service
Flowise is a low-code platform designed for building and deploying complex language model workflows through a visual, node-based interface. It functions as an orchestrator for autonomous multi-agent systems, allowing users to construct conversational pipelines by connecting language models, memory stores, and external tools on a drag-and-drop canvas. The platform distinguishes itself through its support for sophisticated agentic patterns, including supervisor-worker delegation and iterative reasoning strategies. Users can design directed acyclic graphs to manage conditional branching, state p
FlexGen is an inference engine for large language models designed for high-throughput execution on single or multiple GPUs. It functions as a framework for managing model execution through a combination of memory offloading, weight compression, and pipeline orchestration. The system enables the execution of models that exceed available GPU memory by moving tensors and caches between GPU memory, system RAM, and disk storage. It utilizes 4-bit weight quantization to reduce the memory footprint of model parameters, allowing for increased batch processing capacity. The project covers distributed
The Swiss Army Knife of Offline AI. Chat, Speak, and Generate Images - Privacy First, Zero Internet. Download an LLM and use it on your mobile device. No data ever leaves your phone. Supports text-to-text, vision, text-to-image