Solutions · AI/ML workloads
Source Training and Inference Capacity On-Chain
AI teams are capacity-constrained and price-taking. VirtEngine inverts the relationship: buy a listed plan outright, or describe what you need and let providers bid — capping the price with attribute matching if you prefer. 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.
At a glance
The short version for ai/ml workloads
-
Leverage
Demand-side market power
Post an order; provider daemons bid against it. Competition happens per order, continuously — not per contract cycle.
-
Verification
Hardware you can check
Benchmark records let you verify measured accelerator performance before accepting a bid — never trust the SKU alone.
-
Scale
A real HPC path for large runs
On-chain jobs with walltime and partition requirements execute on SLURM-class clusters — supercomputing-grade interconnects included.
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.
In one view
Audience-specific visual
Each solution gets its own explanatory figure — not the same template art.
- Training / Inference / Fine-tuning
- GPU memory ≥ X GB
- Region + topology
- → matching capacity
The mechanism
How the protocol carries it
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 or taking a listed plan.
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). Assess counterparty evidence, any offer-specific VEID proof, escrow terms, and on-chain history together; none removes all risk.
How it works
The path through the protocol, step by step
Select each step — the panel walks the sequence in order, from first action to settled outcome.
Step 1 · Describe
Describe the workload
Resources, accelerator classes, region, and attribute constraints go into a deployment spec — training or inference alike.
Step 2 · Require
Set placement requirements
Benchmarked hardware, audited attributes, or attested enclaves for proprietary weights and sensitive training data.
Step 3 · Fund
Fund escrow and post the order
Bids arrive from matching providers; you choose the winner. Escrow you control backs the lease.
Step 4 · Run
Run and monitor
Metered usage records and settlement state are queryable chain data. Idle budget returns when the deployment closes.
Step 5 · Protect
Protect the model itself
Require attested enclave execution and encrypted secret delivery for proprietary work. Counterparty risk stays bounded by VEID verification and on-chain reputation.
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
-
Describe the workload
Resources, accelerator classes, region, and attribute constraints in a deployment spec.
-
Set placement requirements
Benchmarked hardware, audited attributes, or attested enclaves as needed.
-
Fund escrow and post the order
Bids arrive from matching providers; you choose the winner.
-
Monitor usage and settlement
Metered records and settlement state are queryable chain data.
Questions
Asked about ai/ml workloads
Does this fit training runs, inference, or both?
Both. Interactive and inference workloads ride standard leases; large training runs fit the HPC path — on-chain jobs with walltime and partition requirements on SLURM-class clusters through native adapters.
How do we verify the GPUs behind a bid?
Through published benchmark records (x/benchmark): measured performance data attached to the provider's on-chain identity, so you accept bids on evidence rather than SKU names.
How are models and training data protected?
Where the selected offer supports it, require attested enclave execution (x/enclave) and encrypted secret delivery (x/encryption); review provider attributes, audits, history, and any disclosed VEID proof requirement before committing.
Related
Continue from here
- What is AI training?
- GPU compute providers
- HPC on VirtEngine
- Confidential computing
- Escrow & settlement explained
More solutions