Quant Trading Firm Achieves 0.3ms Risk Compute Latency

Meridian Capital runs quantitative strategies across equity, fixed income, and derivatives markets. Their risk infrastructure faced a structural problem: intraday risk engines required dedicated, low-latency GPU access, but end-of-day regulatory batch jobs needed burst capacity that sat idle during market hours.

The existing answer was two separate clusters — one for real-time, one for batch. This met the latency requirement but doubled hardware costs and created a compliance headache: regulators required demonstrable data segregation between trading desks, which physical separation satisfied but at significant cost.

Cerio.ai implemented a composable pool with strict QoS tiers. The real-time risk tier received guaranteed GPU reservations with hard latency SLAs enforced at the scheduler level — no batch workload could interfere. After market close, the real-time tier's reserved capacity released into the batch pool, which burst to absorb overnight regulatory scenario computation.

Cryptographic workload isolation at the hardware virtualization layer replaced physical separation for data segregation. Each desk's models and position data ran in isolated GPU contexts with a complete, timestamped audit trail of every resource allocation.

The compliance team passed a regulatory audit on the new architecture within 90 days of deployment, with the audit trail satisfying examiners' data segregation requirements. Intraday risk latency hit 0.3ms — below the 1ms threshold required for the firm's hedging strategies. CapEx for the combined real-time and batch workload dropped 40% versus the previous two-cluster model.