Three independently measurable layers. Each layer stands alone. Together they deliver ~51% total facility savings.
Between active workloads, traditional datacenters leave GPUs in a hot-idle state consuming 600–700W. They stay ready at full power "just in case" the next job arrives — wasting energy every second.
ConserveMode™ continuously monitors workload patterns and predicts when the next job will arrive. It drops GPUs to a deep dormant state (75W) and wakes them just before they're needed — zero latency impact, 89% less power.
Why this is significant: Most enterprise datacenters run at 40–60% GPU utilization — meaning 40–60% of all GPU-hours are idle. That is the entire window ConserveMode™ captures with Layer 1.
| Request Type | % | Power | Contribution |
|---|---|---|---|
| Exact cache hit | 51.4% | 0W | 0W |
| Semantic cache hit | 15.2% | 0W | 0W |
| Miss → 7B model | 26.0% | 200W | 52W |
| Miss → 13B model | 5.0% | 350W | 18W |
| Miss → 70B model | 2.3% | 700W | 16W |
| Weighted Avg | 100% | 86W | 87% saved |
Traditional inference runs every request through the GPU — even when the answer was computed moments ago. ConserveMode™ caches both exact and semantically equivalent results.
With a mixed enterprise workload, 66.6% of all requests are cache hits requiring 0W. The remaining 33.4% are routed to the smallest model capable of answering — 78% go to 200W models, only 7% reach full 700W inference.
Think of it like broadcast television: compute the result once, deliver it to everyone who needs it. One transmitter, any number of receivers.
IT equipment represents 40–60% of total datacenter energy. When you reduce IT load by 87–89%, you also reduce the cooling burden — and enterprise cooling is massively inefficient.
The Enterprise Inefficiency Opportunity: Enterprise and government datacenters operate at PUE 1.5–1.8 vs. 1.1–1.2 for hyperscale. Cooling uses 30%+ of total energy vs. 7% at hyperscale. This is where the largest untapped savings live.
The ~51% figure assumes: 80% cache hit rate, 50% GPU utilization, enterprise PUE 1.6. Actual results vary by facility profile.
| Layer | Mechanism | Savings | Measurement |
|---|---|---|---|
| 1 — Idle States | Predictive power scheduling between workloads | 89% | 700W → 75W per node |
| 2 — Inference | Semantic cache (66.6% hits = 0W) + ML model routing | 87% | 650W → 86W weighted avg |
| 3 — Total Facility | Combined IT + cooling compound effect | ~51% | Whole-facility · 80% cache hit rate |