AI that runs on your hardware —
and tells you when it isn't sure.
Anantus builds private, self-hosted AI coding models for teams that can't send their code to the cloud. Our models will be 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.
The problem
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.
What we're building
A coding model you can run entirely inside your own walls, built around three commitments.
Private by design
Designed to run on your own NVIDIA hardware, on-premises, with no internet connection required. Your code, prompts and outputs never leave your network. Nothing phones home.
Honest about uncertainty
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.
Trained on code that ran
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.
Where we are
Research and development. Status as of October 2026: the data pipeline and the evaluation tooling are built, and we have trained 100M-parameter pilot models on our own cluster to check the training setup, including single-node against multi-node runs. Training at product scale has not started yet. 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.
How we work
Every claim we make is a measurement with a record behind it: paired comparisons on the same tasks, run both ways, with statistics; same-vs-same controls; and refuted hypotheses reported alongside the wins. Our measurement methodology is public.
Our lab
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, GLM-5.3-Flash on four nodes taken from 47.7 to 98.8 tok/s on code after moving to knapcio's open-source stack, plus our own changes, and weight loading cut from 452 s to 34 s after fixing the file-to-GPU copy path.
Free tool: Is your GB10 stuck in low-power mode? A 45-second check. One file, no network access, with healthy-vs-stuck reference numbers from our cluster and the power-drain sequence that fixes it.
Running your own GB10 or DGX Spark cluster?
The same expertise is available as engineering services: multi-node bring-up, throughput tuning and performance debugging on Grace-Blackwell hardware.
Need AI that stays inside your walls?
Early access, research collaboration, or questions about our work: one email reaches the team directly.
contact@anantus.net →We reply within one business day.