For HPC & AI Data Centers

Turn idle accelerators into sellable capacity.

OptOps reads real GPU telemetry, packs MIG slices, tunes power and clocks, and reclaims idle silicon — up to 30–38% more efficiency from the fleet you already own, without buying a single new accelerator.

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verified_userRead-only & sovereigncloud_offAir-gap capablehandshakePay on verified gainstimerInstalls in minutes

memoryGPU fleet optimization

bolt2.46 kW

gpu-0

34% SM412 W

gpu-1

31% SM405 W

gpu-2

29% SM418 W

gpu-3

33% SM409 W

gpu-4

30% SM411 W

gpu-5

28% SM408 W

6 GPUs · 12 MIG slices scattered · avg 31% SM utilisation

01 · The problem

Your fleet is half idle. The meter isn’t.

Compute demand is exploding and power is the new ceiling. You can’t buy your way out — accelerators are scarce and the grid is capped. The only fast lever is getting more from the fleet you already own.

30–50%

typical GPU utilisation in AI clusters

— NVIDIA, 2026

$25–40K

per H100 — idle capacity burns CapEx

— Introl

power is becoming the binding constraint on data-centre buildout

— Goldman Sachs

~2×

data-centre electricity consumption by 2030 — 485 → ~950 TWh

— IEA

monitor_heartOne H100, as your metrics see it

the idle gap

Allocated to tenants100%

scheduler view · card handed out whole · “busy”

Actually computing (SM-active)32%

idle — burned CapEx

silicon view · model parked in VRAM · barely computing

A GPU can hold a model in VRAM while doing almost no computing — allocation says busy, the silicon is idle. That gap is $25–40K per H100 per year (Introl, 2025).

view_in_ar

Accelerators allocated whole

Many scheduling setups allocate an entire GPU per job — leaving unused compute and memory capacity stranded.

trending_up

Provisioned for peak

Tenants reserve for peak demand and sit idle off-peak. The buffer becomes permanent waste.

memory_alt

“Loaded” ≠ “active”

A GPU can hold a model in VRAM while doing almost no computing. Allocation metrics say busy — the silicon is idle.

02 · Why today’s tools fall short

Everyone can see idle GPUs. The hard part is safely eliminating the waste.

Your stack already measures utilisation and places jobs — and idle stays high. Seeing waste and safely removing it are two different jobs.

What you already have

DCGMGrafanaRun:aiKueueNVIDIA DRA

They monitor, allocate and orchestrate — but the optimization loop is still missing.

What’s still missing

arrow_forwardSomething that safely tunes power and packs MIG — continuously
arrow_forwardRight-sizing that adapts as tenant jobs change
arrow_forwardReclaiming idle GPUs without killing running jobs
arrow_forwardExecution — not another utilisation dashboard

The gap we close—the same Analyze → Advise → Act engine, GPU-aware.

03 · Accelerator optimization

The levers that turn idle silicon into revenue

Every lever is risk-scored, opt-in, and applied under your policies and SLAs — with zero risk to running jobs.

grid_view

MIG packing & fractionalisation

Slice and pack GPUs so multiple tenants and jobs share one card safely — more workloads per accelerator.

content_cut

Right-size memory & compute

Match VRAM and SM allocation to what jobs actually use — not the whole 80 GB card.

speed

Real SM-utilisation uplift

Lift actual compute activity, not just allocation — turn “loaded” GPUs into working ones.

bolt

Power & clock tuning

Tune GPU power caps and clocks to each workload — large energy savings with no loss on the jobs that matter.

device_thermostat

Power-aware scheduling

Place and pack jobs to run inside your power and thermal budget — never blow the cap.

pause_circle

Design-partner beta

Safe preemption + checkpointing

Pause lower-priority jobs to free GPUs for paying demand, then resume exactly where they stopped — no recomputation, no lost hours.

04 · Power & efficiency

Power is your ceiling. We raise it.

For a power-bound data center, efficiency is capacity — every watt saved is a watt you can sell.

boltFacility power envelope

grid cap · 1.0 MW · fixed

Serving compute

420 kW

Idle + untuned burn

310 kW

Cooling overhead

210 kW

Sellable headroom

60 kW

Illustrative split for a 1 MW hall — actual gains depend on workload mix and are measured per deployment.

30–38%

less energy

Measured in deployment — from power/clock tuning + MIG packing.

~41%

of AI-cluster power goes to accelerators

And they keep drawing it even when idle (research, 2026) — tuning GPU power is the single biggest efficiency lever you have.

05 · How it works

Air-gapped in. Savings out.

Sits alongside your stack, on-prem or air-gapped. Automation is opt-in, under your policies.

rocket_launch

Deploy

Air-gap ready

Read-only agent, air-gapped deployment. Sits alongside your HPC stack — no infrastructure changes to start.

monitoring

Analyze

First scan

Reads DCGM and accelerator telemetry — real SM and memory utilisation and power draw, per GPU and per job.

lightbulb

Advise

Ongoing

MIG packing, power and clock tuning, and right-sizing recommendations — each tagged with ROI and a confidence score.

savings

Save

First week

Apply safely under your policies and SLAs. Up to 30–38% more efficiency from the fleet you already own.

conveyor_beltHow a change actually ships

no hidden automation

memoryYour accelerators

cloud_offAir-gapped deployment

neurologyOptimization engine

fact_checkRisk-scored recs

how_to_regYou approve

boltOptOps acts

write access exists only for approved changes — scoped per action, temporary, revocable, fully logged

account_tree

Slurm-native, today

Live

We started where the world’s GPUs actually run — the scheduler behind virtually every supercomputer and national research cluster.

hub

Kubernetes GPU, next

In development

The same engine, extending to K8s GPU fleets — backed by our production-proven Kubernetes platform. Design partners get first access.

06 · Safe & sovereign

Built to protect production — and your data.

The first question every operator asks: “what if this touches a tenant’s job — or their data?” The architecture is the answer.

visibility_lock

Read-only by default

The agent needs no write access to deliver value — monitoring reads metrics, specs and usage only.

verified

Confidence score on every change

Each recommendation carries a risk score; nothing low-confidence is ever proposed for automation.

how_to_reg

Human approval workflow

You approve every change. Write access is scoped per action, temporary, revocable and fully logged.

undo

One-click rollback

Any applied change can be reverted instantly — production safety is the architecture, not a feature.

cloud_off

On-prem & air-gap capable

Runs fully inside sovereign, air-gapped environments — built for national labs and residency-bound compute.

lock

Data never leaves your cluster

Only infrastructure telemetry is read — never tenant applications, models, or data.

07 · Proof

Real GPUs. Real power savings.

30–38%

energy saved

power/clock tuning + MIG packing, measured in deployment

MIG + spot

demonstrated live

autonomous right-sizing of jobs on real GPUs

National HPC

supercomputing deployment

accelerator/HPC proof with an authorised national supercomputing programme

fact_check

Where we actually are — the honest version

Live and proven on Kubernetes + GPU, delivering real accelerator utilisation and power savings today. Deployed with an authorised national supercomputing programme as our accelerator/HPC proof. Preemption and checkpointing are in design-partner beta — we’d prove them on your cluster together, on your workloads, before you commit. We don’t overclaim a finished HPC product: we bring a proven engine and prove it on your fleet.

08 · The economics

Idle accelerators are unsold inventory.

For a data center that sells capacity, every point of utilisation is revenue. Sweat the asset.

storefront

More sellable capacity

Pack + right-size → more tenants per GPU → more revenue from the same fleet. No new hardware.

electric_bolt

Reclaimed power headroom

Efficiency frees watts → deploy and sell more compute inside the same envelope. For a power-bound facility, efficiency is capacity.

event_upcoming

Defer the next buildout

Get more from installed accelerators before the next multi-million-dollar cluster — better ROIC per rack.

handshakeCommercial model: pay on verified savings and gains. Our upside is tied to yours — no savings, no bill.

09 · Roadmap

GPU today. All compute tomorrow.

One engine, expanding — Kubernetes to GPU to HPC to every substrate that runs AI.

TodayLive

Kubernetes + GPU

checkMIG packing, power & clock tuning
checkRight-sizing + spot placement
checkDelivering 30–38% efficiency
checkLive with customers

NowBeta

HPC & GPU cloud

checkPreemption + checkpointing (beta)
checkNational supercomputing deployment
checkSovereign / on-prem / air-gapped
checkMulti-tenant, SLA-aware

2027+Vision

AI tokens & inference

checkInference & token-cost optimization
checkGPU-fleet & AI-workload orchestration
checkPolicy- and intent-driven
checkThe optimization layer for all compute

10 · Next steps

Let’s prove it on your cluster.

Beta on your cluster

Preemption + checkpointing + power optimization on a scoped slice of your fleet — measure the real gain before you commit.

Opportunity report

An infrastructure analysis that identifies high-impact optimization opportunities and quantifies potential savings in dollars and watts.

Deep-dive + commercials

Power-consumption and accelerator-optimization detail, and a pay-on-verified-gains model agreed in writing before anything begins.

See how much capacity is hiding in your fleet

We’ll walk your cluster view, resource footprint, and cost insights live — then scope a beta on a slice of your fleet and measure the real gain.

Book a Demo

or contact us — founder-led, operator-built

Running Kubernetes in the cloud too? The same engine does node consolidation, spot automation, and live migration.