You bring the workload. We take care of the GPU rental.

We do not need to own inventory first. The service searches verified GPU rental marketplaces, scores true cost, configures the winning rental, and earns only when capacity is viable enough to quote.

Customer input

Target price

Engine output

Best true cost

Concierge fee

Success-based

Capture the best live GPU price

Best rate right now across the venues we track live. Indicative snapshot — not a guaranteed lock until we confirm.

Subsidiary operating arm

The shop counter stays practical; the parent brand stays deep.

Parent lab owns the IP, research story, topology animation, and algorithm platform.
GPU concierge subsidiary sells quote matching, setup, monitoring, and migration support.
No required Stripe checkout: start with manual quote, invoice, payment confirmation, then provision.
Revenue target is practical service income without diluting the research brand.

Concierge model

Arbitrage discovery, setup, and cycling instead of owning GPUs.

Customer gives a workload, GPU class, hours needed, and price to beat.
The engine ranks marketplace offers by hourly rate, reliability, storage, bandwidth, setup time, and interruption risk.
We configure the winning rental, monitor it, and migrate if the current machine stops being optimal.
FPGA is a premium search accelerator later; the first business can run as concierge sourcing.

Asia Compute Desk

Regional sourcing for scarce accelerator capacity in Asia-Pacific time zones.

Regions Hong Kong · Singapore · Tokyo · Seoul · Taiwan

Capacity B200 / H100 / A100 clusters

Commercial model Commission on secured capacity or documented savings

Quote intake

The minimum information needed to decide if a job is worth taking.

Workload GPU class Setup fee ($) Monitoring fee ($/mo)

Request a GPU quote

No checkout required. We source capacity first, then confirm payment manually.

We do not provision until capacity, true cost, and payment method are confirmed.

Compute arbitrage model

Cross-venue arbitrage: we source the cheapest of Vast.ai, RunPod, Lambda & TensorDock, then price to match or beat the competition while protecting a minimum margin.

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Customer target ($/hr) Hours needed Min margin (%) Beat competition by (%) Reliability penalty weight Cycle threshold (% better) Commission on savings (%)

Projected profit & captured income

Projected = forecast at the current spread, not realized cash

Projected · 24h—

Projected · 48h—

Projected · 72h—

Captured income (real) $0.00 No GPU rentals captured yet — fills when a sourced rental is invoiced & paid.

Venue	GPU	Raw $/hr	True $/hr	Client $/hr	ML score	Decision

FPGA search pilot

Use hardware acceleration where marketplace search becomes the bottleneck.

The FPGA idea belongs in fast offer matching, route scoring, and cycling decisions across many markets, not upfront inventory.

Practical rule: do concierge sourcing first, save every quote and outcome, then prove whether FPGA acceleration improves quote win rate enough to rent an F2 unit.