03 Operate · Managed AI Platform
Your AI platform team, without building one internally.
Peregrine operates the platform layer between GPU infrastructure and your AI applications — so your engineers build models, not clusters.
The gap
There is a lot between bare GPUs and production AI.
GPUs alone do not serve models. Drivers, container runtimes, schedulers, storage, serving frameworks and MLOps tooling all need to be installed, integrated, secured, monitored and upgraded — continuously.
Peregrine operates that layer as a monthly managed service, on infrastructure you own, Peregrine capacity, public cloud or a mix.
- APPLICATION
- MLOPS
- MODEL SERVING
- STORAGE
- SCHEDULING
- KUBERNETES / SLURM
- CONTAINERS
- DRIVERS + CUDA
- BARE GPUs
Capabilities
Nine areas of platform operation.
CLUSTER MANAGEMENT
- Kubernetes or Slurm cluster operations
- Node lifecycle, drivers and CUDA environments
- Configuration management and documentation
- Change control for platform updates
GPU SCHEDULING
- Fair-share and priority scheduling
- GPU partitioning where supported
- Queue and job management
- Utilisation-aware placement
MODEL SERVING
Model serving environments can provide OpenAI-compatible APIs.
- Inference endpoints for production models
- Autoscaling and health management
- vLLM, Triton and TensorRT-based serving where appropriate
- Versioned model rollout
OBSERVABILITY
- GPU utilisation, memory and thermal monitoring
- Job, queue and endpoint metrics
- Centralised logging
- Alerting and reporting
MLOPS
- Fine-tuning pipelines
- Experiment and artefact tracking integration
- Container image management
- CI/CD for model deployment
USER & QUOTA MANAGEMENT
- Projects, teams and namespaces
- Resource quotas and limits
- Identity integration with your directory
- Multi-tenancy where required
UPGRADES
- Driver, CUDA and runtime upgrades
- Kubernetes and Slurm version management
- Patch management with maintenance windows
- Compatibility testing before rollout
SECURITY OPERATIONS
- Access control and audit logging
- Network segmentation and private connectivity
- Secrets management
- Designed to support customer security and governance requirements
CAPACITY MANAGEMENT
- Utilisation trend reporting
- Capacity forecasting with your team
- Expansion planning across owned, Peregrine and cloud capacity
- Cost visibility by project
Any hardware
One platform. Any infrastructure.
Operate Peregrine infrastructure, customer-owned infrastructure or hybrid environments through a consistent AI platform.
- Peregrine GPU infrastructure
- Customer-owned
- Colocated
- Public cloud (AWS, Azure, GCP)
- Hybrid
Built for modern AI workflows
Technology ecosystem.
Target and supported environments can include the frameworks and tools your team already uses. Model serving environments can provide OpenAI-compatible APIs.
- NVIDIA CUDA
- Docker
- Kubernetes
- Slurm
- PyTorch
- TensorFlow
- Jupyter
- vLLM
- NVIDIA Triton
- TensorRT
- API access
- Object storage
- Private networking
- Commercial model
- Monthly managed service
- Operates on
- Any hardware — owned, Peregrine, cloud, hybrid
- Your team
- Builds and ships AI
- Peregrine
- Runs the platform layer
Discuss Managed AI Platform.
Tell us where your GPUs are — or will be — and what your team needs to run. We will scope the operating model and monthly service.