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AI Platform & GPU Infrastructure

Kubernetes, GPU capacity planning, model serving and LLMOps that your team can operate after we leave.

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GPUs are expensive, and so is a platform nobody can operate.

Teams buy or rent GPUs before knowing what their models need, then discover that serving, scaling, monitoring and upgrading models is a platform job. Without a platform, every AI project builds its own stack and costs are hard to see.

We size the hardware from your models and traffic, build the serving platform on Kubernetes in your environment, and set up the LLMOps practices your platform team will own.

You may need this if

  • You're about to buy or reserve GPUs and aren't sure how many.
  • Each AI team runs models its own way.
  • GPU utilization or cost per request is unknown.
  • Model updates are manual and hard to roll back.

What we deliver

  • 01

    Capacity planning

    GPU memory and throughput estimates from your models, context lengths and traffic, with options for quantization and batching.

  • 02

    Kubernetes platform

    GPU node pools, scheduling and isolation in your cloud, data centre or air-gapped environment.

  • 03

    Model serving

    Autoscaled model servers behind an API gateway with authentication and quotas.

  • 04

    LLMOps

    A model registry, versioned releases, evaluation gates and rollback.

  • 05

    Observability and cost

    GPU utilization, latency, errors and cost per team or use case.

  • 06

    Handoff

    Infrastructure-as-code, runbooks and on-call training for your platform team.

Reference architecture

A reference design, not a fixed product. Discovery adapts it to your systems and constraints.

A shared model-serving platform

  1. Consumers

    • Internal apps
    • Assistants and agents
    • Batch jobs

  2. Gateway

    • API gateway
    • Authentication
    • Quotas and routing

  3. Serving

    • Model servers
    • Autoscaling
    • Model registry

  4. Kubernetes

    • GPU node pools
    • Scheduling
    • Storage

  5. Hardware

    • Cloud GPUs
    • On-premises GPU servers

Operations

  • Metrics and logs
  • Cost reporting
  • Evaluation gates
  • Infrastructure-as-code

How an engagement usually runs

  1. Discovery2 weeks

    Models, traffic and constraints analysed; GPU sizing and a platform design with costs.

  2. Pilot6–8 weeks

    A production serving platform for the first models, with monitoring and runbooks.

  3. PodMonthly

    More models and teams onboarded, cost optimization and upgrades.

Constraints we design for

Air-gapped clusters
Container images, drivers and model weights installed offline from an internal registry.
GPU availability and budget
Designs that work with the hardware you can get, with quantization where quality allows.
Shared infrastructure
Isolation and quotas, so teams don't starve each other.
Existing platform standards
We build on your Kubernetes distribution, CI/CD and monitoring.

Typical stack

  • Kubernetes
  • NVIDIA GPU Operator
  • vLLM
  • Triton Inference Server
  • KServe
  • Prometheus
  • Grafana
  • Terraform
  • Argo CD

We use the tools you already run where they fit. Names are for reference only.

Tell us about your systems

An engineer reads every message and replies within one business day.

Start a conversationHow we engage