Built to scale
Infrastructure designed for AI workloads.
QNTOR orchestrates compute, models and enterprise data across scalable infrastructure — with elastic inference, distributed training and observability built into every layer.
Built to scale
Infrastructure designed for AI workloads.
QNTOR orchestrates compute, models and enterprise data across scalable infrastructure — routing every request to the right runtime, on the right hardware, inside your security perimeter.
Reference architecture
Application Layer
QNTOR API
Agent & Model Orchestration
Model Runtime
GPU Compute
Enterprise Data Layer
Cloud native
Cloud-native. GPU accelerated.
QNTOR is designed to leverage scalable cloud infrastructure for model training, fine-tuning and high-performance inference.
GPU Compute
Accelerated pools sized for training and inference workloads.
Elastic Model Inference
Scale endpoints with traffic, per model and per environment.
Distributed Training
Multi-node fine-tuning jobs with checkpointing and recovery.
Secure Storage
Encrypted object storage for datasets, artifacts and indexes.
Vector Search
Low-latency retrieval over enterprise knowledge collections.
Autoscaling
Queue-aware scaling that keeps latency and cost predictable.
Infrastructure designed for AWS environments with GPU-accelerated compute and modern AI/ML services. QNTOR makes no claim to certifications or vendor partnerships that have not been obtained.
Reference deployment
Designed for scalable deployment on AWS.
An illustrative topology showing how the QNTOR platform can operate inside an enterprise AWS environment.
Users / Enterprise Applications
Amazon API Gateway
Application Services
AI Orchestration Layer
Amazon Bedrock
Amazon SageMaker
EC2 GPU Instances
Amazon S3
Amazon Aurora
Amazon OpenSearch
Security layer
- AWS IAM
- AWS KMS
- Amazon VPC
Monitoring
Amazon CloudWatch
Metrics, logs and traces for models, agents and GPU utilization.
Acceleration
Accelerated AI inference.
QNTOR supports GPU-accelerated AI workloads designed to take advantage of NVIDIA compute infrastructure — from single-node experimentation to multi-node production serving.
Technology references only. QNTOR does not claim an NVIDIA partnership.
Accelerated workloads
- LLM inference
- Fine-tuning
- Embedding generation
- Batch processing
- AI agent workloads
Performance
Built for production workloads.
Sample values shown below are illustrative demo figures, not real production benchmarks.
Inference Latency
182
ms p50
GPU Utilization
72
% of pool
Requests / Second
9.4
k sustained
Token Throughput
48
k tokens/s
Infrastructure Cost
-31
% vs. baseline
Deployment
Deploy AI your way.
Fully managed AI infrastructure.
Start in minutes on managed GPU capacity with built-in observability and autoscaling.
Deploy within the customer's cloud environment.
Run the control plane and model runtime inside your own accounts and network boundary.
Combine private infrastructure with scalable cloud GPU resources.
Keep sensitive data in place while bursting heavy training and inference to the cloud.
Build your AI infrastructure with QNTOR.
Deploy private AI models and intelligent agents on infrastructure designed for the next generation of enterprise software.