Open-weight language models get a private chat service on your own server, and llama.cpp serves its browser UI the moment the server starts. Conversations appear in a sidebar with search, and each answer shows its token count, generation time, and tokens per second, so you can see how a model really performs. Code replies render in highlighted blocks with a copy button, and messages can be edited, regenerated, forked, or deleted. A Models page lists what the server can run with context sizes and load status, and Settings cover themes, system messages, display options, tools, agentic behavior, import and export, and sampling controls for temperature, top K, top P, and penalties. Remote MCP servers connect by URL, and uploads let multimodal models read files and images. Behind the UI sit OpenAI and Anthropic compatible chat endpoints, embeddings, reranking, function calling, JSON schema constrained output, parallel decoding, and continuous batching for several users at once. Quantized models run on plain CPUs, and official images cover CUDA, ROCm, Vulkan, and other accelerators. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Unsloth puts the whole open-model workflow in one browser tab: pick a model, chat with it, train it, and ship it. Search a model hub, load a GGUF, and talk to it with web search, deep research, file uploads, and sandboxed Python or shell execution that asks before anything risky runs. Line up two models side by side to compare answers, or generate images and video with diffusion models. When a base model is not quite right, a five step wizard sets up fine-tuning with LoRA, QLoRA, FP8, or full training, and a live dashboard charts loss, gradient norm, step progress, and GPU memory. Data Recipes turn PDFs, CSVs, and Word files into question and answer pairs or instruction datasets without hand labeling. Finished models export to GGUF or safetensors for llama.cpp, vLLM, Ollama, and LM Studio. An access token screen exposes an OpenAI-compatible endpoint so Claude Code, Codex, and OpenCode can drive local models. Secure HTTPS links reach the interface from any device. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache-2.0 and AGPL-3.0 licensed.

Xinference lets a team swap a hosted model API for models running on its own machines, with the same client code and a web console for managing them. The Launch Model page lists 300+ models across language, vision, embeddings, reranking, images, audio, video, and experimental world generation, and a Recommend configuration button fills in engine, size, format, and quantization for your hardware. Custom models, including traditional machine learning models, can be registered beside the built-in ones. Chat models can run on vLLM, SGLang, TRT-LLM, Transformers, llama.cpp, or MLX across CPUs, GPUs, and Apple Silicon. Workers and replicas can be added to serve one large model across several machines. Running Models shows each replica and a built-in chat window, and Performance Tests measure throughput and latency at several concurrency levels, with saved runs to compare. Endpoints speak the OpenAI and Anthropic protocols with streaming and tool calling where a model supports it. API keys, user management, and an audit center cover access control. The interface is available in five languages. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.

One dashboard and one API address for every local model you run, across llama.cpp, vLLM and MLX: that is llamactl. Create an instance in the web UI, pick a backend, and the server assigns ports and API keys, starts the model, and shows its health next to every other instance you run. Models download straight from HuggingFace inside the dashboard, in GGUF for llama.cpp or Safetensors for vLLM, so stocking a new model never means juggling command lines and file paths. Your applications keep talking to familiar endpoints, because llamactl exposes OpenAI chat completions and Anthropic messages routes and sends each request to the model named in the call. A llama.cpp router instance can serve several models from one process and load them on demand, and idle instances stop on a timer or make room through LRU eviction when the GPU gets crowded. Groups cap how many heavy models run at once, backends can live in Docker containers with GPU access, and instances on other machines appear in the same central dashboard with automatic routing. API keys minted in the UI guard both inference and management calls. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.