Use GPU capacity more efficiently
Continuously align active GPU capacity with the workload the fleet is actually serving.
Intelligent runtime orchestration for AI inference
Make the inference fleet fit the workload.
Schedatrix optimizes the inference fleet as a system—not as a collection of independently managed model endpoints. It helps AI infrastructure teams reduce unnecessary GPU resource usage as demand changes—improving fleet and energy efficiency while working with the serving stack and router already in place.
The opportunity
One inference fleet. One system-level optimization.
Modern inference stacks are excellent at serving and routing requests, but capacity is often managed model by model. As demand and workload mix change, Schedatrix optimizes across the fleet to find more efficient resource configurations.
Adapt as workload behavior changes
Respond to shifts in demand, model usage, and service requirements over time.
Complement the existing AI stack
Add fleet-level intelligence without replacing the serving platform or model router.
Current results
Measured on GPUs. Tested further in offline audits.
Controlled studies across multiple GPU platforms and evaluation settings show substantial fleet-efficiency gains, with offline audits providing additional evidence across workload conditions and longer horizons.
GPU Platform: 2×H100
Measured silicon · vLLM
33.9%
vs Always-On
Lower active GPU-seconds
Measured in a controlled two-model vLLM serving experiment. Tested latency and error objectives held, with scoped quality matching the Always-On large-model reference.
GPU Platform: 8×A100
Measured silicon · NVIDIA Dynamo
38.1%
vs NVIDIA Dynamo GlobalPlanner
Lower GPU-seconds with joint fleet control
Measured in a controlled three-model serving study using a joint fleet-control policy, with comparable tested TTFT and near-zero errors.
Offline Audit: Simulation
20.0%
vs independent per-pool scaling
Median reduction across five runs
Lower modeled provisioned GPU-seconds across five controlled seeds, with a 16.8%–21.1% range and consistent opportunity signals across all five runs.
Offline Audit: Simulation
18.3%
vs independent per-pool scaling
Longer-horizon robustness check
Lower modeled provisioned GPU-seconds than independent per-pool scaling in a two-hour simulated-time study, with the advantage remaining after modeled transition overhead.
About the results: Silicon studies report measured active GPU-seconds; offline audits report modeled provisioned GPU-seconds. These measure GPU capacity usage — not facility energy or cloud cost. Results vary with workload, hardware, serving stack, and service objectives.
Energy efficiency
Lower GPU usage also reduced measured GPU-board energy.
In controlled H100 and A100 studies, Schedatrix reduced measured GPU-board energy while maintaining the tested serving objectives.
Current evidence: Measured GPU-board energy moved in the same favorable direction as GPU fleet usage.
What Schedatrix does
Optimize the inference fleet as a system.
Schedatrix brings system-level intelligence to AI inference infrastructure. It continuously adapts GPU resource decisions as workloads and service needs change—helping infrastructure teams get more from the fleet they already operate.
System-level viewCoordinate resource decisions across the inference fleet.
Stack-compatibleWork alongside existing serving, routing, and scaling infrastructure.
Workload-responsiveAdjust fleet capacity as demand and workload mix change.
Schedatrix Efficiency Audit
Find your next inference-efficiency opportunity.
Start with a lightweight, read-only analysis of a sanitized workload trace. The Schedatrix Efficiency Audit provides a practical first look at where your inference fleet may have room to operate more efficiently.
1
Profile the workload
Analyze request timing, eligibility mix, and workload shape.
2
Establish the baseline
Reconstruct current fleet provisioning and resource usage.
3
Quantify the opportunity
Estimate potential GPU resource savings with assumptions and relevant baselines clearly stated.
4
Design the pilot
Define a controlled validation on real infrastructure around the strongest identified opportunities.
Design partners
Bring fleet-level optimization to your inference stack.
Schedatrix is preparing founder-led design-partner engagements with teams operating self-hosted, multi-model inference. Start with an Efficiency Audit to identify high-value optimization opportunities, then validate the strongest opportunities through a controlled pilot.
Interested in working with us? Tell us briefly about your inference stack and current challenges.