GPU Allocation Optimizer
MNT-LO allocates GPU workloads with microsecond-scale latency and zero conflicts. A black-box deterministic engine — proprietary, production-ready, measurable.
Per-workload allocation decision. 750 workloads processed in ~24ms — validated across 3 consecutive benchmark runs per tier.
Every workload that fits is allocated. Zero waste from solver indecision. SLA platinum across all tiers and all benchmark runs.
Zero constraint violations. Zero rejected workloads. Zero corrupted GPU nodes — across all tiers and all benchmark runs.
Send a JSON payload describing your GPU resources, workload constraints, and allocation preferences. No configuration files, no training data, no warm-up.
MNT-LO's proprietary deterministic engine resolves the allocation internally. No randomness, no heuristics — the same input always produces the same optimal output.
Receive a clean allocation map in microseconds. Each GPU assigned, zero conflicts, ready to deploy. Integrate directly into your orchestration pipeline.
Deployed as a high-performance, stateless Docker container. Native Linux integration designed to secure IP privacy with zero system overhead.
{
"workloads": [
{ "id": "job-001", "mem": 40, "priority": "high" },
{ "id": "job-002", "mem": 24, "priority": "medium" }
],
"gpu_pool": [
{ "id": "gpu-01", "mem": 80 },
{ "id": "gpu-02", "mem": 40 }
]
}
{
"allocations": [
{ "workload": "job-001", "gpu": { "id": "gpu-01", "mem": 80 }, "score": 0.5, "type": "direct" },
{ "workload": "job-002", "gpu": { "id": "gpu-02", "mem": 40 }, "score": 0.6, "type": "direct" }
],
"meta": { "allocated": 2, "rejected": 0, "efficiency_score": 1.0 },
"elapsed_ms": 0.24,
"sla": { "sla_level": "platinum", "allocation_rate_percent": 100.0 }
}
| Scope | Decision Variables | What the engine solves in practice | Plan |
|---|---|---|---|
| Memory | Workload → GPU | GPU Memory Allocation: fit jobs within each card's physical VRAM limit, eliminating residual waste. | Starter / Growth |
| Memory + Time | Workload → GPU → Queue | Temporal Orchestration: predict contract duration, manage concurrent queues, and ensure no card sits idle between jobs. | Starter / Growth |
| + Region | Workload → GPU → Queue → Region | Geographic Distribution: decide which datacenter or availability zone the workload should run in to mitigate network latency and rack thermal stress. | Scale |
| + Tenant | Workload → GPU → Queue → Region → Tenant | Business Isolation (Multi-Tenant): total governance. The engine routes across different customer contracts, priority SLAs, and governance boundaries in real time. | Enterprise |
Interested in a pilot, custom licensing, or technical evaluation? Send your details and we'll respond within 24 hours.