AI infrastructure. Built for scale.
Deploy LLMs, train models and run inference workloads across distributed GPU infrastructure with automatic scaling and cost optimisation.
Deploy it in one file
Core handles multi-cloud orchestration, load balancing and failover automatically. Everything below is real config.
services:
- name: llm-inference
image: vllm/vllm-openai:latest
env:
MODEL: meta-llama/Llama-2-70b-chat-hf
TENSOR_PARALLEL_SIZE: 4
resources:
gpu: nvidia-a100:4
memory: 256GB
providers:
- gcp: us-central1
- akash: gpu-cluster
autoscaling:
min_replicas: 1
max_replicas: 10
target_gpu_utilization: 70Built for production workloads
GPU orchestration
Automatic GPU provisioning and management across multiple cloud providers with intelligent workload placement.
- Multi-GPU support
- Automatic failover
- GPU utilisation tracking
- Cost-optimised placement
Model serving
Deploy models as scalable API endpoints with automatic batching, caching and load balancing.
- Auto-scaling inference
- Model versioning
- A/B testing support
- Sub-100ms latency
Training pipelines
Distributed training with automatic checkpointing, experiment tracking and resource optimisation.
- Distributed training
- Automatic checkpointing
- Experiment tracking
- Spot instance support
Model registry
Version and manage models with built-in artifact storage and metadata tracking.
Cost optimisation
Reduce GPU costs through spot instances and intelligent workload scheduling.
Monitoring & observability
Real-time GPU metrics, model performance tracking and detailed usage analytics.
Multi-framework support
Native support for PyTorch, TensorFlow, JAX and popular inference engines.
Ready to get started?
Deploy on infrastructure we run ourselves, governed entirely by EU law.