Autonomous optimization formodern compute infrastructure.

OptOps turns idle GPUs and HPC capacity into usable, sellable compute — MIG packing, power and clock tuning, right-sizing and safe preemption. Read-only by default, air-gap capable.

The Complete Platform for HPC & AI Data Centers— and Kubernetes

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.

Book a Demo Get an Opportunity Report

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.

FAQ

Your Questions, Answered

OptOps is an autonomous optimization platform for HPC and AI data centers, GPU clouds, and research clusters. It reads real accelerator telemetry, packs MIG slices, tunes power and clocks, right-sizes jobs, and reclaims idle silicon — turning the fleet you already own into more sellable capacity, without buying a single new accelerator. The same platform also optimizes Kubernetes clusters.

Not by default. The agent is read-only and needs no write access to deliver value. Every recommendation carries an ROI estimate and a confidence score, and you approve every change. When you do enable an action, write access is scoped per action, temporary, revocable, and fully logged — and any applied change can be rolled back in one click.

Yes. OptOps runs fully inside sovereign, air-gapped environments and sits alongside your existing HPC stack — no infrastructure changes to start. It reads only infrastructure telemetry, never tenant applications, models, or data. The platform has completed an independent third-party security assessment, and ISO certification is in progress.

OptOps is Slurm-native today — the scheduler behind virtually every supercomputer and national research cluster. Support for Kubernetes GPU fleets is in development, built on our production-proven Kubernetes platform, and design partners get first access.

In deployment, power and clock tuning plus MIG packing have delivered 30–38% energy savings. Before you commit, an opportunity report quantifies the potential savings for your fleet in dollars and watts — and commercials are pay-on-verified-gains, agreed in writing before anything begins.

Every lever is risk-scored, opt-in, and applied under your policies and SLAs. Safe preemption with checkpointing (in design-partner beta) pauses lower-priority jobs to free GPUs for paying demand, then resumes them exactly where they stopped — no recomputation, no lost hours.

Yes. For EKS, GKE, AKS, or any CNCF-compliant cluster, OptOps consolidates nodes, automates spot, and rightsizes pods continuously — and on AWS it uses container live migration to do it without disrupting running workloads. Teams typically see a 30–70% reduction in Kubernetes infrastructure costs.