[ Prover farm with GPU scheduler ] →
H100 / A100 / 4090 pool, deadline-aware job prioritization, NUMA-aware placement, 75%+ per-card utilization in typical setups.
> solutions / zk
Proof generation is GPU-bound, deadline-bound, parallel. Dropped jobs = missed blocks. We build the farm, the queue, the retries, and per-circuit benchmarks on SP1, RISC Zero, Boundless, Brevis.
We arrive with a working stack: SP1 and RISC Zero as the RISC-V zkVM baseline, Boundless and Brevis as decentralized prover marketplaces, Jolt and Halo2 for custom circuits. For each farm we wire up a GPU scheduler with deadline-aware prioritization, kv-cache for circuit segments, retry queues with exponential backoff, and missed-proof-window alerts.
The whitespace we sit on: the "operator for operators" on prover marketplaces. Succinct, Boundless and Brevis launched as marketplaces in late 2025; no one occupies the "we run your GPU farm on these marketplaces with a signed SLA" position. We take it.
ZK subset. The platform layer is identical across ICPs.
Concrete deliverables for ZK teams. Each ships end-to-end with repo, IaC and runbooks.
H100 / A100 / 4090 pool, deadline-aware job prioritization, NUMA-aware placement, 75%+ per-card utilization in typical setups.
Idempotent submit, exponential backoff, dead-letter queue with per-circuit analysis, alert on pre-deadline ETA miss.
Measurements on SP1 / RISC Zero / Jolt per circuit: time-to-prove, peak memory, optimal GPU type. Artifact cache for precompiles.
Marketplace registration, bidding strategy per circuit type, reputation tracking, payout reconciliation, marketplace switching by marginal cost.
Proving stack for your own rollup: coordination across sequencer, batcher and provers, L1 finality monitoring, stall playbook.
After handoff the pager lives with us. Coverage tuned for ZK farms:
What we move without losing payouts or skipping proofs.
Move from a single prover marketplace to a portfolio of 2-3: differentiate by circuit type, hedge against single-venue downtime.
Farm moved off AWS / GCP onto dedicated GPU at Latitude.sh / DataPacket: typical 50% cost-per-proof reduction, no deadline misses.
Parallel shadow proving for correctness, gradual cutover by circuit type, latency baseline pinned along the way.
Prover capacity 5x in 7-10 days: H100 burst sourcing, auto-onboarding into marketplaces, post-season wind-down playbook.
Quantization-aware proof generation where the circuit allows: 30% reduction in time-to-prove, re-verify check per batch.
From an external prover service onto your own farm: breakeven calc, gradual cutover, audit of key material and precompiles.
Anonymized. NDAs cover names; the numbers are real.
Three coverage levels. For production proving with a signed deadline SLA we recommend Silver or higher.
| Tier | Response p95 (Sev-1) | Coverage | Incident report | Engineer hours / mo |
|---|---|---|---|---|
| Bronze | 30 min | Business hours, 5×8 | Within 48h | 40 |
| Silver | 15 min | 24/7 on-call rotation | Within 24h | 80 |
| Gold | 5 min | 24/7 with dedicated engineer | Within 12h | 160+ |
Across all of them. SP1 and RISC Zero for the RISC-V zkVM approach, Jolt and Halo2 for custom circuits, Boundless / Brevis / Succinct as the marketplace layer. If you have a collab with a specific provider, we plug into their toolchain.
Sev-1. Escalation in 5-15 min (by tier), root cause in 12-24h, postmortem with concrete action items. Architecturally we try to catch misses early: an alert on ETA >70% of the proof window triggers a pre-emptive migration to a free GPU.
Depends on the circuit and target throughput. Baseline for one production circuit: 4-8 H100 or 16-24 RTX 4090. For a marketplace bid: start with 8-16 H100 and scale by win-rate. Send the circuit type and target proofs/hour; we'll send a sized estimate in 24h.
Yes. It's one of our whitespace bets. We register as your operator on Boundless / Brevis / Succinct, run the bidding strategy, watch reputation, reconcile payouts. Staking material and payout wallets stay with you.
Yes. That includes sequencer + batcher + prover as a coordinated pipeline, L1 finality observability, and a stall playbook. We can build the stack from scratch or take an existing setup into operations.