VirtEngine

Solutions · AI/ML workloads

Source training and inference capacity on-chain

AI teams are capacity-constrained and price-taking. VirtEngine inverts the relationship: describe what you need, let providers bid, verify hardware through published benchmarks, and pay only for metered usage from escrow you control.

Who this is for: ML teams that need training or inference capacity without hyperscaler lock-in.

The problem

The problem: allocation queues and opaque pricing

GPU allocation at major clouds means waitlists, committed-use contracts, and prices set by scarcity you can't see. Specialized GPU clouds improve price but reintroduce single-vendor risk — and rarely let you verify the hardware behind the SKU.

How VirtEngine addresses it

Grounded in what the protocol actually does

Demand-side market power

Post an order specifying accelerators, memory, region, and required attributes; provider daemons bid against it. Competition happens per order, continuously — not per contract cycle. Benchmark records (x/benchmark) let you verify measured performance before accepting a bid.

Batch jobs on real HPC

Large training runs fit the HPC path: on-chain jobs with walltime and partition requirements executing on SLURM-class clusters through native adapters — supercomputing-grade interconnects included, no re-platforming on either side.

Protect the model itself

For proprietary weights and sensitive training data, require attested enclave execution (x/enclave) and encrypted secret delivery (x/encryption). Counterparty risk is bounded by VEID verification and on-chain reputation in both directions.

Economics

Economics

You fund escrow; providers draw against it only as metered usage settles — hourly records, 24-hour dispute window, anomaly detection before submission. Idle budget returns to you when the deployment closes. Cost control is structural, not a billing-alert afterthought.

Getting started

The path in

  1. Describe the workload

    Resources, accelerator classes, region, and attribute constraints in a deployment spec.

  2. Set placement requirements

    Benchmarked hardware, audited attributes, or attested enclaves as needed.

  3. Fund escrow and post the order

    Bids arrive from matching providers; you choose the winner.

  4. Monitor usage and settlement

    Metered records and settlement state are queryable chain data.

Related

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