Ante 0.2: a ~15MB coding agent that manages llama.cpp for you — point it at a GGUF and the whole agent loop runs offline

Hello~

We just shipped Ante 0.2, and the part I think this community will care about most is offline mode. We wanted local to be a first-class way to run the agent, so Ante manages the inference engine itself:

• Managed llama.cpp. Ante installs a pinned, checksum-verified official build matched to your hardware — Metal on Apple silicon; CUDA, Vulkan, or CPU on Linux — and offers upgrades when the pinned version changes.

• GGUF discovery. It scans ~/.ante/models, the llama.cpp cache, and the Hugging Face cache for models already on your disk, and detects llama servers already running on local ports.

• Memory estimation before load. It estimates RAM/VRAM from model size, KV cache, and context window before anything loads, with live progress for download, tensor loading, and GPU offload.

• Server lifecycle handled. Boot, supervise, and shut down from the TUI — or keep the server running on exit and reattach later.

• No API key, no account. Once the model file is on disk, inference needs no network connection at all. Set ANTE_TELEMETRY=off for a fully disconnected run.

Getting started is /offline-mode in the TUI, or one step from a script:

ante --offline-model /path/to/model.gguf "your prompt here" 

There's also ante serve --offline-model <path> to load a model once and share it across all connecting clients, and vision works automatically if an mmproj-*.gguf sits next to the model file.

The loaded model registers as an ordinary provider next to hosted ones, so you can switch mid-session with /providers — draft against a frontier model, flip to local for a sensitive repo, flip back. Any OpenAI-compatible server (Ollama, vLLM, LM Studio, your own) plugs into the same catalog.

On honesty about local performance: we benchmark local models with the same harness, pinned public builds, and auditable runs as frontier ones. Qwen3.6 27B — a 17 GB download — scores 56.2% on Terminal-Bench 2.1 across 445 trials. That's a real gap from frontier models, and we'd rather publish the number than pretend otherwise. Live results are at https://antigma.ai/eval, with a curated list of what actually runs well before you download 17 GB.

The rest of 0.2, briefly: everything ships in one self-contained binary (including a rewritten ripgrep and local PDF/OCR), it has the harness features you'd expect (steering, direct multi-provider support, subagents, skills), and --profile lets you strip it down to a pi-style minimal setup. Since the preview launch it has processed almost 7 trillion tokens, with a new version shipped nearly every day.

Links:

• Offline mode docs: https://ante.run/local/overview
• GitHub: https://github.com/AntigmaLabs/ante
• Why the harness matters (small Terminal-Bench experiment vs. opencode, pi, and hermes): https://antigma.ai/blog/2026/08/04/harness-matter

Happy to answer anything about how the engine management works, what quants we've verified, or where offline mode still has rough edges — it's under active development and there are some.