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AI / ML
Enterprise on-premises GPU clusters run at 10–15% utilization. That's not a hardware problem. Orion brings time-slicing, gang scheduling, and workload-aware reclamation to your existing infrastructure, so development, training, fine-tuning, and inference share the same cluster without dedicated reservations.
AI/ML infrastructure outcomes
Up to 4× more researchers per GPU node. Time slicing lets multiple training jobs, notebooks, and inference endpoints share expensive accelerators.
From request to running GPU environment. Not days of waiting on IT tickets, JIRA queues, and manual provisioning cycles.
Zero
Model weights, training data, or checkpoints leave your VPC. Customer-hosted deployment keeps proprietary AI inside your perimeter.
GPU infrastructure lifecycle
🔬
GPU time slicing for development
A single high-end GPU can serve multiple concurrent notebook sessions, each with guaranteed GPU access. Researchers iterate on models without waiting in queue, and your accelerators stop sitting at 15% utilization between training runs.
🔀
Distributed training orchestration
Multi-node distributed training jobs scheduled across your GPU fleet automatically. Orion handles node allocation and network topology awareness, so your ML engineers focus on model architecture, not infrastructure YAML. Bring your framework of choice via Helm.
🔒
Model weights never leave your VPC
Fine-tune proprietary LLMs on private data without streaming IP to hosted APIs. Training data, checkpoints, and model weights stay inside your environment from first token to last: no external inference endpoints, no vendor training-set leakage.
🚀
Inference and model serving
Deploy inference endpoints alongside training workloads on the same infrastructure. Orion dynamically allocates GPU fractions based on demand: scale serving capacity up during peak hours and reclaim resources for training overnight, automatically.
What teams run on Orion
LLM fine-tuning
LoRA, QLoRA, and full fine-tuning of foundation models on your own data, with GPU time slicing for concurrent experiments
Computer vision
Object detection, segmentation, and image generation models, multi-GPU training with automatic compute orchestration across your fleet
RAG pipelines
Embedding generation, vector indexing, and retrieval-augmented inference, co-located on the same GPU infrastructure as your models
Reinforcement learning
RLHF and reward model training with dynamic GPU scaling. Burst to multi-node when experiments demand it, scale back when idle.
Classified AI processing
Air-gapped LLM deployment for defense and intelligence. On-prem infrastructure with zero external network dependency and full data sovereignty.
Model eval & benchmarking
Spin up ephemeral GPU environments for model comparison and evaluation. Tear down automatically when benchmarks complete.
Ship more models without buying more GPUs.
Up to 4 researchers per GPU node. Sixty seconds from request to running environment. No IT tickets, no queue.
