Default Kubernetes
Strong for product orgs and device plugins — multi-team fairness, packing quality, and cost accountability usually remain unfinished.
GPU Capacity Infrastructure
Private preview · design partners
GPU capacity isthe new scarceresource.
We're building the capacity layer for shared fleets — allocate with policy, not politics.
02 / The problem
Your cluster can look busy and still fall behind.
Shared GPU fleets fail in predictable ways — especially when multiple teams compete for the same pool.
UtilizationReported
Useful workReality
utilization ≠ progress
Illustrative — your fleet will differ
01
Idle and fragmented capacity
GPUs look allocated. Useful work still waits.
02
Priority inversion
Research holds capacity while product work queues.
03
Over-ask without accountability
Every team requests more than they use. The safe move is to overbuy.
04
Capacity planning by guesswork
Wait times and waste stay unexplained. Hardware decisions become politics.
Why Ainek exists
Busy is not the same as useful. Schedulers place jobs; they do not govern scarce shared GPU capacity as an economic resource across teams. That gap is why we're building Ainek.
03 / Why now
Default schedulers were not built for this load.
01
Larger, costlier fleets
GPU pools are bigger, more shared, and more expensive than when queue defaults were enough.
02
Mixed work on one pool
Training, fine-tuning, and inference compete for the same GPUs. Contention is normal, not exceptional.
03
Spend must be explainable
Finance and leadership ask what the fleet delivered. “The cluster is busy” is no longer an answer.
04 / Status quo
Schedulers place jobs. Shared fleets still need capacity control.
Owning Kubernetes or Slurm answers how work gets placed. It does not answer who gets scarce GPUs, when, or what the fleet actually delivered.
Slurm
Trusted for research queues — product and research on one fleet still need clearer policy, reclaim, and spend visibility.
05 / Why care
What you can defend upstairs — if this works.
Private preview. No vanity proof. If the abstraction is right, these are the stakes platform leaders brief on — results will depend on your fleet and policy.
01
Useful work from the fleet
Capacity spent on real work, not reserved idle or fragmented scraps.
02
Wait pressure
How long priority jobs wait before they get the hardware they need.
03
Defendable spend
Waste and effective cost of completed training and inference — explainable to finance.
04
Fairness under contention
Multi-team load follows policy instead of whoever shouted last.
06 / What we're building
Capacity governance for shared fleets.
The abstraction: a capacity layer that governs scarce GPUs across teams — not another scheduler. Four verbs name the job. Implementation depth lives on Product.
01
Observe
Requested vs useful capacity, idle hold, wait pressure.
02
Allocate
Quotas, priorities, and fair-share before GPUs are committed.
03
Pack / reclaim
Cut fragmentation and idle hold so waiting work can run.
04
Explain
Usage attributed so platform and finance can see the spend.
Depth: Product
07 / Where it sits
Above your scheduler. You keep the stack.
Ainek is being built as a capacity layer above Kubernetes or Slurm — not a rip-and-replace platform. Placement stays with the scheduler you already run.
- 01Works alongside Kubernetes and/or Slurm — you do not throw them away.
- 02Policy and capacity decisions sit above job placement you already trust.
- 03Built for shared fleets; depth on architecture and deploy is on Product.
08 / Fit
Who early access is for.
For
- Organizations operating a shared GPU fleet
- Multiple teams competing for the same pool
- Kubernetes and/or Slurm in production
- A clear owner: ML platform, AI infra, or GPU platform
- Teams willing to build as design partners — not buy a finished SKU
Not for
- Single-team experiments on a laptop or a few cloud instances
- Teams with no shared cluster to govern
- Buyers who only need a raw job scheduler
- Anyone needing a generally available product today
Build it with us.
Request early access. Tell us your fleet and stack. If you're a fit as a design partner, we'll follow up — including an honest no if not.