AI Infrastructure
Reproducible training environments, GPU cluster management, and model deployment pipelines that don't break between runs. We bring infrastructure rigor to AI workloads.
AI infrastructure is a mess. Training environments drift between runs. GPU drivers break after updates. Model deployments work on the researcher's machine but fail in production. CUDA versions, Python packages, and system libraries form a dependency nightmare that "just pip install" can't solve.
Nix fixes this at the root. Every dependency — from CUDA toolkit to Python packages to system libraries — is pinned and reproducible. Same inputs, same outputs, every time. No more "it trained fine last week."
GPU cluster management
NixOS-based GPU cluster deployments with proper driver management, CUDA toolkit pinning, and multi-user scheduling. Bare metal or cloud — same declarative configs that actually reproduce.
Training environments
Reproducible ML environments where every dependency is pinned. From PyTorch versions to CUDA drivers to system libraries. Your training run from last month? You can rerun it exactly.
Model deployment
Production inference pipelines built with Nix. Container images with exact dependency trees, reproducible model serving, and deployment configs that work identically across environments.
AI agent infrastructure
We run AI coding agents in production ourselves. LLM orchestration, tool integration, sandboxed execution environments — we've solved these problems for our own systems first.
Need AI infrastructure that reproduces?
GPU clusters, training environments, model deployment — tell us what you're building and we'll figure out the infra.