HybridLLM trainingLLM inferenceFine-tuningDev sandbox

Shared Enterprise GPU-as-a-Service

A self-service platform that pools scarce GPUs across teams. The core problems are fair sharing, high utilization, and clear cost attribution — a scheduling and governance problem more than a hardware one.

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Business objective

Maximize utilization of expensive GPUs across many teams with fair access and transparent cost attribution.

Personas
Platform engineerEngineering managerFinOpsML engineer
Key assumptions
  • ·Many teams with uneven, spiky demand share a finite pool.
  • ·Mix of interactive (notebooks), batch (training), and serving workloads.
  • ·Cost must be attributable per team/project.
Logical architecture

Rendering diagram…

Design

Compute

Partition GPUs with MIG/time-slicing for small/interactive jobs; reserve whole GPUs/nodes for large jobs. Priority classes and preemption keep utilization high.

Network

Tiered: a training fabric for large jobs, standard networking for interactive/serving. Isolate tenant traffic.

Storage

Per-team quotas on shared parallel + object storage; shared datasets deduplicated; user workspaces.

Kubernetes

Multi-tenant Kubernetes with namespaces/quotas, a fair-share queue (Kueue/YuniKorn), and a self-service request portal.

Security

RBAC and namespace isolation, quota enforcement, and per-tenant secrets. Interactive access is authenticated and audited.

Observability

Per-tenant utilization and cost, queue wait times, idle detection, and fairness metrics feeding showback/chargeback.

High availability
Pool resilience: draining a node reschedules jobs; queues survive control-plane blips. Serving workloads get replica-level HA.
Disaster recovery
Workspaces and datasets backed up; the platform is reproducible from IaC so a cluster can be rebuilt and reattached to storage.
Capacity
Sized to aggregate demand with a target utilization; partitioning raises effective capacity. Track queue wait as the signal to expand.
Cost profile
The whole point is cost efficiency: chargeback drives good behavior, idle reclaim and partitioning raise utilization, and reporting exposes waste.
Risks
  • mediumNoisy-neighbor contention degrades interactive latency without isolation.
  • mediumHoarded reservations leave GPUs idle; needs idle reclaim + chargeback pressure.
  • lowQuota model too rigid, frustrating teams and pushing them off-platform.
Implementation phases
  1. 1

    Pool + queue

    Multi-tenant cluster, fair-share scheduler, namespaces and quotas.

  2. 2

    Partitioning

    MIG/time-slicing and priority/preemption for mixed workloads.

  3. 3

    Self-service

    Request portal and guardrails for teams to onboard themselves.

  4. 4

    Showback

    Per-tenant utilization + cost reporting and idle reclaim.

Validation checklist
  • Fair-share holds when demand exceeds supply.
  • Interactive latency stable under a noisy batch neighbor.
  • Idle capacity is detected and reclaimable.
  • Per-team cost attribution reconciles with total spend.
Recommended GPUs for this pattern
NVIDIA

H100 SXM5

80 GB · 700 W

NVIDIA

A100 80GB SXM

80 GB · 400 W

NVIDIA

L40S

48 GB · 350 W

AMD

Instinct MI300X

192 GB · 750 W