GPU Rental Platforms: Raising Per-GPU Output with Storage
The hidden cost of GPU rental is switching idle time: GPUs wait on loading whenever models or tenants change. Measured with FX100, effective compute utilization at the 20-switches/hour level rose from 46.7% to 62.8%.
The idle ledger of multi-tenant platforms
Rental platforms sell GPU-hours, but loading time during tenant model switches is pure loss. R1's measured modeling: at 20 switches/hour on NFS/slow storage, effective compute utilization is just 46.7% — half the compute is idling.
With FX100 the same level reaches 62.8%: single-model loads 30% faster and 8-GPU concurrent loads 16% faster wall-clock (R1). For a platform billing by GPU-hour, that is directly more sellable inventory.
The full solution with KV tiering stacked on
Switch acceleration solves "change fast"; KV tiering solves "come back fast": tenant session recovery without re-compute (8.6–20×, R2) lets the platform sell context retention as a value-added service — the self-hosted version of the context-cache pricing the big API vendors run.
Ratio anchor: one FX100 per 8 nodes; choose among the three product tiers (FX100/200/300) by port bandwidth and IOPS, with fully-populated reference prices public.
FAQ
How was the utilization figure computed?
R1 models the effective token output rate across model switches: the share of compute time actually producing tokens, with switching/loading as the loss term. The report contains the full method.
How is tenant data isolated?
The weight pool is shared (one copy per open-source model); KV is isolated by tenant namespace; block-layer encryption and access control are configured on demand at the business stage.
Is a heterogeneous fleet (NVIDIA + domestic GPUs) supported?
FX is a standard NVMe-oF target, decoupled from GPU models; the software adaptation layer already covers ROCm/Ascend/MetaX (R1/R5/R9).
Data sources (verifiable)
Related reading
- Model-Load Acceleration: A Measured Path from 23 Minutes to 2.5 Minutes
- The Storage Technology Behind Context Caching
- Inference Storage Planning for a Thousand-GPU AI Datacenter
Joint test first, decisions second: gate-based acceptance with built-in stop-loss
The full costing model is provided as reproducible Python after NDA — customers can rerun it with their own parameters. Every key figure on this site carries a report ID and is open to third-party verification.
This site presents business-cooperation information and constitutes neither an investment offer nor any promise of returns. Measured data come from signed / official test reports (see the Evidence Library); vendor specs, public sources and estimates are labeled as such.