Live
Local Inference Engine
Token-by-token WebSocket streaming with GGUF quantization, prompt caching, and sub-200ms time-to-first-token.
Run open-weight LLMs locally via quantized GGUF execution, train custom LoRA/QLoRA adapters on your data, and orchestrate local coding agents — zero cloud dependencies.
Everything AI Studio ships with today — and what's being built next.
Token-by-token WebSocket streaming with GGUF quantization, prompt caching, and sub-200ms time-to-first-token.
Dataset ingestion, background fine-tuning pipelines, real-time loss tracking, and GGUF adapter merging.
Unified D:/models repository shared across all local tooling, so nothing gets duplicated on disk.
A drop-in local endpoint for any OpenAI-SDK app — same request shape, no API key, no code changes.
Pull, convert, and requantize open-weight checkpoints to GGUF directly from the UI — no terminal needed.
Multi-session chat with live adapter switching, system-prompt presets, and token-level telemetry.
An opencode-style, tool-calling agent with real terminal access — running entirely against your local models.
Live VRAM, RAM, and GPU utilization per loaded model, so you know what fits before you load it.
Three phases, shipping in order — nothing is deferred past its phase.