Anantus builds private, self-hosted AI coding models for teams that can't send their code to the cloud. Our models are trained on code that has actually been run, not just collected, and they are designed to say plainly when an answer can't be confirmed instead of presenting a guess as fact.
AI coding assistants mostly run in someone else's cloud, which means sending your source code and your questions off-site. For banks, healthcare, defense suppliers and anyone working under data-residency rules, that is often not allowed. Running a model locally solves the privacy problem but creates another: smaller local models guess more often, and a confident wrong answer about your code is worse than no answer.
A coding model you can run entirely inside your own walls, built around three commitments.
Runs on your own NVIDIA hardware, on-premises or fully air-gapped. Your code, prompts and outputs never leave your network. Nothing phones home.
Designed to separate answers it can confirm from answers it can't, and to label the second kind clearly, so your engineers know what to trust and what to check.
Our training corpus is built by actually running real-world code in isolated sandboxes and recording the outcome of each document, rather than learning from whatever happens to be on the internet.
Research and development. We have built the data pipeline and the evaluation tooling, and trained early small-scale pilot models on our own cluster; the first results are encouraging and we are now scaling training. We publish what we measure, including what doesn't work. If your team needs a private coding model and wants to help shape it, we'd like to hear from you.
Every claim we make is a measurement with a record behind it: paired comparisons on the same tasks, run both ways, with statistics; negative controls; and refuted hypotheses reported alongside the wins. Our measurement methodology is public.
We develop on an eight-node NVIDIA GB10 Grace-Blackwell cluster that we own and run ourselves: ARM64, unified memory, multi-node inference and training over our own network fabric. Getting large models to run well on this platform taught us a great deal, and we share it openly in our field notes and benchmarks: a 4.4× collective-communication fix on the prefill path, +58% decode throughput from a CUDA-graph fix, and weight loading cut from 7.5 minutes to 35 seconds.
The same expertise is available as engineering services: multi-node bring-up, throughput tuning and performance debugging on Grace-Blackwell hardware.
Early access, research collaboration, or questions about our work: one email reaches the team directly.
contact@anantus.net →We reply within one business day.