An AI Infrastructure Now Within Reach
Introducing the QAI
Democratizing the AI Cluster for All
Powerful, Affordable and Scalable AI Cluster with Customizable Options for your business.
Hyperscalers have made it harder for smaller organizations to have their own private AI cluster. It is about time for you to take charge of your organization's data and operate your own AI infrastructure.
With QAI, Quantea is bringing private AI to every business.
Affordability
Starting at just a fraction of the cost of similar clusters makes the QAI accessible to a wide range of businesses.
Customizability
Flexible hardware and software options to choose from. Choose the GPUs, operating systems, and storage size that best fit your workload.
Scalability
Start with a 4-node cluster to keep initial investments low, then expand to 100+ nodes as your business grows.
Data Privacy
Be able to leverage the power of AI without having to give up on your organization's proprietary and confidential data.
QAI is the clear winner for every business
When it comes to capabilities and features that matter for businesses, QAI is the clear winner when it comes to building your own private AI.
Quantea
Fully Integrated Cluster Solution
QAI
Quantea Intelligent Cluster
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Affordable AI Capabilities
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Fully Integrated, Pre-configured
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Integrated IDS & Network Monitoring
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Easy to scale without expertise
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QInsight Software Included
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High Speed Storage Included
Best Value
NVIDIA
DGX
Systems
-
High Upfront Costs
-
Requires additional setup
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Requires additional solutions
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Complex and expensive scaling
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No built-in analysis tools
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No Storage Included
Cloud AI
Infrastructure
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Expensive for long-term usage
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Requires cloud management
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Security tools add complexity
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Scalable but at a high cost
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General network analysis tools
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Storage at a prohibitive cost
AWS
EC2 P3
Compute Instances
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High hourly costs
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Requires cloud configuration
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Paid security add-ons
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Scalable but at high recurring costs
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Requires custom built tools
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Storage at a prohibitive cost
How much more do others cost?
QAI provides a complete solution: compute, storage, networking, security all meticulously integrated to create an AI infrastructure.
A comparison below of how much more others would cost for:
•8 x Nvidia H100 GPUs, 500TB High Speed Storage
•Performance Monitoring
•Intrusion Detection System
Quantea
Fully Integrated Cluster
$
0
Essentials Included
-
Affordable AI Capabilities
NVIDIA
$
+500k
Additional Cost
-
High Upfront Costs
Google Cloud
$
+1.7M
3YR-PLAN @ $48k/month
-
High hourly costs
AWS
$
+3M
3YR-PLAN @ $84k/month
-
Expensive for long-term usage
Why Rent When You Can Own
Cloud costs skyrocket as users have established their methods and AI workloads increase in volume.
Predictable and Lower Long-Term Costs
On Premises
Quantea AI Cluster (QAI)
While the initial investment in on-premises hardware is high, the total cost over time decreases significantly. Organizations only pay for maintenance, upgrades, and energy after the initial purchase, avoiding recurring fees.
Cloud
AWS, Lambda, GCP, Azure
Cloud Providers charge ongoing fees for compute, storage and data transfers. These can accumulate over time to a large sum. For workloads running continuously, these costs can surpass the upfront capital expense of an on-prem infrastructure.
Costs During High Utilization for Consistent Workloads
On Premises
Quantea AI Cluster (QAI)
If an organization has continuous, high volume AI workloads (model training or inference), it can fully utilize its on-prem resources, making the infrastructure more cost-effective.
Cloud
AWS, Lambda, GCP, Azure
Cloud is optimal for "bursty" or sporadic workloads, but the consistent use leads to escalating costs, as resources are billed hourly or by usage.
Cost Savings on Storage and Data Transfers
On Premises
Quantea AI Cluster (QAI)
Large datasets remain in-house, avoiding significant data transfer fees. Many cloud providers charge high egress fees for moving data out of their platforms, which adds up for AI applications.
Cloud
AWS, Lambda, GCP, Azure
AI training often requires massive datasets and frequent read/write operations, leading to significant storage and data transfer expenses.
Scalability at No Additional Premiums
On Premises
Quantea AI Cluster (QAI)
Once the cluster is installed, scaling to full capacity incurs no additional cost unless new hardware is needed. Organizations can plan and scale infrastructure to their specific needs without paying a premium.
Cloud
AWS, Lambda, GCP, Azure
Scaling in the cloud often involves premium pricing tiers. Auto-scaling or high-performance configurations can dramatically increase costs.
Savings from Depreciation and Tax Benefits
On Premises
Quantea AI Cluster (QAI)
Hardware is a capital expense (CapEx) and can be depreciate over time, providing tax benefits.
Cloud
AWS, Lambda, GCP, Azure
Cloud expenses are operational (OpEx) and provide no long-term financial leverage.
Compliance and Security
On Premises
Quantea AI Cluster (QAI)
Keeping sensitive data in-house avoids compliance costs associated with securing data in the cloud. This is especially relevant in industries like healthcare or finance.
Cloud
AWS, Lambda, GCP, Azure
Meeting regulatory requirements in the cloud often involves additional services, audits, and costs.
Tailored Infrastructure for AI Workloads
On Premises
Quantea AI Cluster (QAI)
Customizing infrastructure to match specific AI workloads (e.g., GPUs optimized for training or inference) ensures maximum efficiency. Organizations can optimize hardware configurations to reduce wasted resources.
Cloud
AWS, Lambda, GCP, Azure
While flexible, cloud solutions may offer generic hardware that is not fully optimized for specific workloads, potentially increasing runtime and costs.
No Vendor Lock-In
On Premises
Quantea AI Cluster (QAI)
Organizations have full control over their infrastructure and can switch vendors or technologies without significant migration costs.
Cloud
AWS, Lambda, GCP, Azure
Migrating workloads from one cloud provider to another can be expensive and time consuming, often involving additional fees.
comparing QAI and Proprietary Clusters
Choosing QAI has proven benefits over proprietary clusters such as the ones from Nvidia.
Power Efficiency
Strategic component selection by Quantea's integration experts to deliver the most power savings. The entire cluster is built to achieve the lowest energy use and carbon footprint.
Simplified Maintenance
Readily and widely available components are sourced locally to reduce downtime and repair complexity.
Compatibility and Reliability
Seamless integration with a variety of hardware components and software.
Cost Effective
Savings achieved from deep integration and supply networks are passed to the customer.
Power Consumption Comparison
165KW
Quantea QAI
215KW
Nvidia DGX H100 Basepod
Compatibility and Reliability
Seamless integration with a variety of hardware components and software.
Cost Effective
Savings achieved from deep integration and supply networks are passed to the customer.
Quantea AI
INFRASTRUCTURE
CONSULTING SERVICE
Quantea offers cutting-edge infrastructure consulting services tailored for QAI Clusters. Our expertise ensures optimal deployment, performance, and scalability for advanced AI workloads.
tailored Performance
High performance storage and server configuration.
networking
Advanced networking setup for seamless data flow.
Security and monitoring
Continuous monitoring and scalability solutions such as intrusion detection and firewalls.
Let Quantea empower your AI initiative with a robust and reliable QAI Cluster solution.
Quantea Cognition®
400G smart connectivity for AI clusters, included with QAI
Latency and congestion awareness
AI workload distribution mapping using telemetry data from network traffic within the cluster.
Dedicated Ports for Monitoring and Cybersecurity
Direct replica of network traffic for full transparency of activity within the cluster. This is perfect for out-of-band network monitoring applications and intrusion detection systems.
QAI Cluster Metrics
Purpose-built Network and Security Analysis for AI Clusters
With QI, actively monitor metrics that are crucial in affecting the AI inference cluster:
- Inference/training latency: request-response and completion times
- Throughput: requests per second and data volume
- Network packet loss: packet loss and jitter
- Load balancing: traffic distribution and congestion forecast
- Failure rates: error responses and retries
- Unusual traffic patterns
- Congestion and latency spikes
- Service availability: node uptime and service outages
Non-Intrusive AI Training and Inference Monitoring
Installation does not introduce an additional point of failure.
100% accurate copy of network traffic.
Ensure observability even when network is not operating optimally.
Create redundant observation points without compromise.
Deep Automation Capabilities
End-to-end automation of all observability and analysis functions.
Faster response times with less manual involvement.
“Around-the-clock” coverage and actionability.
Integration with existing workflows and tools.
Start your AI infrastructure with QAI today!
Consultation and Recommendation
Inquiries / Live Demonstation