Frontier AI, on compute people own.
IX Foundry is the full-stack lab to train, evaluate, deploy and continuously improve custom models and agents — running on IX's tokenized GPU clusters. Builders get frontier infrastructure. The people who own it earn from every run.
One loop. Two sides.
Foundry is the demand engine of IX: AI workloads generate the revenue that tokenized infrastructure pays out as on-chain yield.
Own
Investors buy fractional shares of GPU clusters on the IX marketplace.
Power
Those clusters become Foundry capacity for training and inference.
Build
Teams train, evaluate and deploy custom agents on owner-backed compute.
Earn
Every metered run settles on-chain and distributes yield to owners.
RL Environments
Turn any task or workflow into a reinforcement-learning environment. Scaffold, iterate, evaluate and publish from a single CLI loop.
- Verifiable, reward-driven tasks
- One loop: init → dev → eval → push
- Community environment hub
Evaluations
Hosted evaluations to benchmark every checkpoint against open models — no infrastructure, no setup.
- Benchmark vs. 100+ open models
- Zero infra to manage
- Public leaderboards at launch
Hosted Training
Fine-tune and post-train large models on IX's tokenized Orbit Clusters — managed end-to-end, from supervised runs to large-scale RL.
- SFT, LoRA & large-scale RL
- Runs on tokenized Orbit Clusters
- Applied research support
Inference
Ship any checkpoint to dedicated or serverless endpoints with native LoRA support — every call metered for on-chain settlement.
- One-click deployment
- Native LoRA adapters
- Metered, on-chain billing
Proof-of-Compute
Every training run and inference call produces a signed usage receipt that settles on-chain — revenue builders can verify, and yield owners can trust.
- Signed usage telemetry
- On-chain settlement
- Public proof-of-revenue
A hub of reusable environments.
Community-built environments for training and evaluating agents on real, verifiable work.
Open-source at the core.
Author verifiable RL environments in Python.
import foundry_envs as feenv = fe.load("terminal-agent")score = env.eval(model)
Distributed post-training, from SFT to large-scale RL.
$ foundry rl train \--env terminal-agent \--model ix-7b \--nodes 4
Verifiable usage receipts, settled on-chain.
$ foundry receipts lsrun-8842 train 4.2h settledrun-8843 infer 1.1m settledepoch-42 yield → owners
Own the compute. Train the future.
IX Foundry is coming soon — the lab where AI workloads become real, verifiable yield.