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The control plane identifies interactive, background, and evaluation work at admission.
AI-NATIVE SOFTWARE · REFERENCE ARCHITECTURE · ILLUSTRATIVE WORKLOAD
Scaling research agents without scaling idle GPU cost.
A reference design for moving variable agent traffic onto a workload-aware inference fabric spanning interactive and batch execution.
REFERENCE DESIGN · NOT A CUSTOMER CLAIMP50/P95Latency reported by percentile
TOK/GPUThroughput reported with hardware
SLOAvailability defined by agreement
THE CHALLENGE
The operating constraint.
Interactive research sessions created unpredictable traffic while evaluation and indexing jobs competed for the same capacity. The team either over-provisioned GPUs or accepted latency spikes during peak periods.
THE SPARK SYSTEMModel, platform, and capacity—designed together.
SPARK introduced policy routing across interactive and batch lanes, compiled the customer model for dedicated serving, and shifted evaluation workloads dynamically according to live production demand.
SOLUTION ARCHITECTUREOne traceable path
01 02 03 04
One traceable path
from signal to decision.
Policy routing
Latency and cost objectives select the appropriate compiled model and capacity lane.
Continuous scheduling
Batch formation, prefix reuse, and capacity-aware scheduling improve GPU occupancy.
Feedback loop
Production traces feed model evaluation and the next approved serving configuration.
DELIVERYFrom baseline
From baseline
to controlled production.
Workload profile
Separated interactive demand, evaluation jobs, cache behavior, and peak capacity requirements.
Engine optimization
Compiled the model and tuned batching, memory policy, and routing thresholds.
Traffic migration
Shifted production traffic progressively with performance and rollback gates.
“A reproducible test must state model, precision, accelerator, context lengths, concurrency, scheduler settings, and measurement window.”BENCHMARK PRINCIPLE · NOT A CUSTOMER TESTIMONIAL