Your AI.
Your Data.
Your Infrastructure.
QNTOR gives enterprises the infrastructure to build, deploy and operate private AI models and intelligent agents using their own data — securely and at scale.
- Private by design
- GPU accelerated
- Enterprise ready
Built on the world's leading AI infrastructure.
- AWS
- NVIDIA
- PyTorch
- Hugging Face
- Llama
- Mistral
- Qwen
Built with technologies from the modern AI ecosystem.
One AI infrastructure layer
Everything you need to build production AI.
From experimentation to production, QNTOR provides the infrastructure required to run enterprise-grade AI systems.
Model Deployment
Deploy leading open-source and proprietary models through a unified infrastructure layer.
Private AI Agents
Build intelligent agents capable of reasoning, retrieving information and executing enterprise workflows.
Enterprise RAG
Connect AI models securely to internal documents, databases and knowledge systems.
Model Fine-Tuning
Customize models using proprietary datasets and optimized GPU training infrastructure.
High-Performance Inference
Run low-latency AI inference using scalable GPU infrastructure.
Observability
Monitor model performance, token usage, latency, infrastructure utilization and costs.
Control plane
One console for models, agents and compute.
Deployments, GPU pools, knowledge bases, API keys and spend — in a single operational view.
console.qntor.com / overview
Overview
Production workspace · eu-west-1 · illustrative data
Active Models
14
+3 this week
Running Agents
38
6 environments
GPU Usage
72%
24 × A100 pool
Requests
4.1M
last 30 days
Average Latency
182 ms
p50 inference
Monthly Compute
$46.2k
-8% vs. prior
Token throughput
tokens / s
GPU utilization
% of pool
Inference requests
req / min
Infrastructure cost
USD / day
p99 latency
412 ms
Cache hit rate
91.4%
Queued jobs
3
Model hub
One platform.
Any model.
Deploy the right model for every workload without rebuilding your infrastructure.
Llama
Meta · open weights
- Deployment
- Private
- Inference
- GPU accelerated
- Fine-tuning
- Available
- API
- Compatible
Mistral
Mistral · open weights
- Deployment
- Private
- Inference
- GPU accelerated
- Fine-tuning
- Available
- API
- Compatible
Qwen
Qwen · open weights
- Deployment
- Private
- Inference
- GPU accelerated
- Fine-tuning
- Available
- API
- Compatible
Gemma
Google · open weights
- Deployment
- Private
- Inference
- GPU accelerated
- Fine-tuning
- Available
- API
- Compatible
Custom Models
Your architecture
- Deployment
- Private
- Inference
- GPU accelerated
- Fine-tuning
- Available
- API
- Compatible
Bring your own model
Import custom checkpoints and serve them through the same API surface.
Autonomous systems
AI agents that understand your business.
Build secure AI agents connected to your company's knowledge, applications and workflows.
User Request
QNTOR Agent
Reasoning Engine
Action / Response
Financial Analyst Agent
Reporting, variance analysis, forecasting support.
Customer Support Agent
Grounded answers from product and policy knowledge.
Compliance Agent
Policy checks and controlled document review.
Research Agent
Multi-step retrieval across internal literature.
Sales Agent
Account context, proposals and pipeline summaries.
Operations Agent
Workflow execution across internal systems.
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
Privacy & security
Your data stays yours.
Enterprise AI should not require giving up control of your most valuable data.
Data Isolation
Workspace-level separation for datasets, indexes and model artifacts.
Encryption
Encryption in transit and at rest across storage and inference paths.
Private Networking
Private endpoints and VPC-scoped connectivity to internal systems.
Access Controls
Role-based access control for models, agents and knowledge bases.
Audit Logs
Traceable records for prompts, retrievals, deployments and admin actions.
Private Model Deployment
Run models inside your own cloud environment or dedicated tenancy.
Encryption in transit and at rest. Role-based access control. Private cloud deployments. Built with enterprise security principles from day one — without claiming certifications that have not been obtained.
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.
Solutions
AI infrastructure for every industry.
Financial Services
Deploy AI agents capable of analyzing financial information while maintaining strict control over sensitive enterprise data.
Healthcare
Run retrieval over clinical and operational knowledge inside private infrastructure with granular access controls.
Legal
Review contracts and case material with grounded models that never leave your security perimeter.
Cybersecurity
Support detection and response workflows with agents connected to internal telemetry and playbooks.
Energy
Apply AI to operational, engineering and asset data across distributed industrial environments.
Enterprise Software
Embed private model inference and agents into your own product without rebuilding infrastructure.
Developer experience
Built for developers.
A single, predictable API surface for models, agents and retrieval — with SDKs, streaming and monitoring built in.
analysis.py
from qntor import Qntor client = Qntor(api_key="YOUR_API_KEY") response = client.chat.create( model="llama", agent="financial-analyst", messages=[ {"role": "user", "content": "Analyze Q3 performance"} ]) print(response.output)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
Infrastructure shouldn't limit your AI.
Choose your models, infrastructure and deployment environment.
GPU-accelerated inference designed for demanding AI workloads.
Maintain control over proprietary enterprise data.
Move between models and infrastructure without rebuilding your application.
Build your AI infrastructure with QNTOR.
Deploy private AI models and intelligent agents on infrastructure designed for the next generation of enterprise software.