# The stack we build to train on

Canonical page: https://keilab.io/ai/

ai infrastructure

A self-hosting math-native compiler, an OS for real boards, LLM inference running on our own language, agent runtimes, and a from-scratch GPU training stack — all in development.

⟳: In development

### [MAX](https://keilab.io/ai/max/)

A self-hosting, math-native language: Unicode operators and a byte-identical self-compile, built for neural workloads, OS work and GPU code.

⊗: In development

### [max-chain](https://keilab.io/ai/max-chain/)

A permissioned proof-of-authority ledger written in MAX, with consensus, ledger and contract logic in one language.

Σ: In development

### [LLM Inference](https://keilab.io/ai/llm-inference/)

Language models on MAX — Qwen3, Falcon-H1, and a clean-room MAX port of an 80B hybrid model that reached 93% of a reference engine's decode speed in our tests.

Ω: In development

### [MAX OS](https://keilab.io/ai/max-os/)

An operating system written in MAX, designed to test research-derived kernel policies against classical ones and to run bare-metal on real boards.

⊙: In development

### [KeiSeiKit](https://keilab.io/ai/keiseikit/)

A model-agnostic coding-agent stack — a native TUI cockpit, a multi-provider agent daemon, a content-addressed cache under every capability call, and a three-phase sleep layer where agents keep improving between sessions.

⊠: In development

### [KeiSeiSDK](https://keilab.io/ai/keisei-sdk/)

Standalone vertical-stack agent runtime + CLI for self-hosted LLM deployments.

∇: In development

### [GPU LLM](https://keilab.io/ai/gpu-llm/)

From-scratch GPU forward and backward kernels for Qwen3-32B; in our tests the layer backward matched a CPU reference (rel-L2 < 5e-6).

### Operating discipline

A local NVIDIA GB10 for development and inference, rented NVIDIA datacenter GPUs for fine-tuning runs. Every cloud launch goes through a pre-flight memory and cost gate before the meter starts. No unattended pods.

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Site index for AI agents: https://keilab.io/llms.txt