// BREAKING AI'S BIGGEST BARRIERSSecure AI Cloud, Built Around Your Workload
QumulusAI designs and deploys single-tenant GPU environments with committed capacity, customer-controlled workloads, and one infrastructure partner accountable from design through operation.
// TRUSTED BY
From Dedicated Nodes to Your Full AI Factory
Start with the deployment model your workload needs, then add Private AI Cloud, orchestration, storage, and networking as required.
// GPU+CPU INFRASTRUCTURECompute
Dedicated GPU and CPU capacity for customer-controlled workloads.
// AI DATA PATHStorage
Object, file, block, local NVMe, and dedicated clusters designed around the AI data path.
// FABRIC + CONNECTIVITYNetworking
Cluster fabric, service traffic, management access, and private connectivity.
// PLATFORM SERVICESTake Dedicated AI into Production
Managed Kubernetes, Lifecycle Management, Secure AI, and AI Factory add orchestration, lifecycle automation, security controls, and production platform capabilities around your compute, storage, and networking foundation.
Managed
Kubernetes
A supported orchestration foundation for dedicated AI infrastructure.
Lifecycle Management
Automated provisioning, validation, telemetry, and lifecycle control for bare-metal infrastructure.
Secure
AI
Design layered controls across firewalls, isolation, access, data protection, operations, and deployment-specific confidential-computing requirements.
AI
Factory
A complete production AI platform spanning software, data, infrastructure, and operations.
// BACKED BY THE BESTNVIDIA Cloud Partner
QumulusAI is an official NVIDIA Cloud Partner, recognized as delivering infrastructure purpose-built for modern AI workloads at production scale. This designation means our platform is built and validated against NVIDIA reference architectures, giving customers faster time to production, consistent performance at scale, and confidence that their AI investments are running on proven, accelerated computing infrastructure.
Learn more at NVIDIA.com
NVIDIA GPUs Configured for Your AI Workload
Choose the NVIDIA platform around what matters most: model development, inference economics, or enterprise operating requirements.
Rack-Scale
Systems
Rack-scale systems deliver the highest performance for large-scale training, reasoning, and inference. Vera Rubin NVL72 and GB300 NVL72 connect 72 GPUs in one NVLink domain with Arm-architecture NVIDIA Vera or Grace CPUs.
Blackwell
Systems
Blackwell systems are built for large-model training, reasoning, high-throughput inference, multimodal AI, and enterprise visual computing.
Hopper
Systems
Hopper systems support large-model inference, training, fine-tuning, and HPC, with H200 providing more memory for larger models and longer contexts.
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GPUs Per Rack: 72
vRAM/GPU: 288 GB HBM4
vRAM Per Rack: 20.7 TB
CPU Cores: 3,168 cores→ Discuss availability with sales.
→ Learn more about AI Cloud. -
GPUs Per Rack: 72
vRAM/GPU: 288 GB HBM3e
vRAM Per Rack: 20.7 TB
CPU Cores: 2,592 cores→ Discuss availability with sales.
→ Learn more about AI Cloud. -
GPUs Per Server: 8
vRAM/GPU: 288 GB
CPU Type: 2x Intel Xeon 6767P 64Cores/128Threads
CPU Speed: 2.4 GHz (base) / 2.8 GHz (boost)
vCPUs: 256
RAM: 3072 GB
Storage: 30 TB -
GPUs Per Server: 8
vRAM/GPU: 192 GB
CPU Type: 2x Intel Xeon Platinum 6960P (72 cores & 144 threads)
CPU Speed: 2.0 GHz (base) / 3.8 GHz (boost)
vCPUs: 144
RAM: 3072 GB
Storage: 30.72 TB -
GPUs Per Server: 8
vRAM/GPU: 96 GB
CPU Type: 2x Xeon Platinum 8562Y+ 32Cores/64Threads
CPU Speed: 2.8 GHz (base) / 3.9 GHz (boost)
vCPUs: 128
RAM: 1152 GB -
GPUs Per Server: 8
vRAM/GPU: 141 GB
CPU Type: 2x Xeon Platinum 8568Y+ 48Core/96Threads
CPU Speed: 2.7 GHz (base) / 3.9 GHz (boost)
vCPUs: 192
RAM: 3072 GB or 2048 GB
RAM Speed: 4800Mhz
Storage: 30 TB -
GPUs Per Server: 8
vRAM/GPU: 80 GB
CPU Type: 2x Intel Xeon Platinum 8468
CPU Speed: 2.1 GHz (base) / 3.8 GHz (boost)
vCPUs: 192
RAM: 2048 GB
RAM Speed: 4800Mhz
Storage: 30 TB -
GPUs Per Server: 8
vRAM/GPU: 94 GB
CPU Type: 2x AMD EPYC 9374F
CPU Speed: 3.85 GHz (base) / 4.3 GHz (boost)
vCPUs: 128
RAM: 1536 GB
RAM Speed: 4800Mhz
Storage: 30 TB
Every Workload Has It's Own Path to Scale
Training, inference, agents, multimodal pipelines, and HPC place different demands on memory, communication, storage, latency, and orchestration. Explore infrastructure paths built around those differences.
Model Training
Distributed GPU clusters for long-running training jobs, parallel communication, and checkpoint flow.
Fine-Tuning
Repeatable environments for data preparation, post-training, evaluation, and model variants.
AI Inference
Reserved serving infrastructure designed around latency, throughput, concurrency, and utilization.
Agentic AI
Infrastructure for repeated model calls, retrieval, tools, session state, and observability.
Multimodal & Video
Balanced compute, media engines, storage, and data movement for video, image, and audio pipelines.
HPC & Simulation
Dedicated GPU clusters for scientific computing, simulation, rendering, and technical workloads.
// INDUSTRY-DRIVEN PRIORITIESBuilt for the Way You Build
Different organizations move at different speeds and operate under different constraints. QumulusAI brings compute, control, and support together around the way each organization builds, scales, and delivers AI.
Inference Providers
Utilization
Repeatable deployments
Customer commitments
Unit economics
AI Startups
Speed
Runway
Flexible entry points
Path to reserved capacity
AI Labs
Roadmap-driven capacity
Research freedom
Large experiments
Growth planning.
Research & Universities
Shared users
Reproducibility
Grant timelines
Scheduling and support
Private AI for Enterprise
Security review
Private data
Governance
Ownership
Public Sector
Security
Procurement
Data location
Documentation
This Isn't Hyperscale. It's Hyperspeed.
While AI hyperscalers are committed to years-long deployment timelines, QumulusAI leverages its partner ecosystem to meet the needs of AI developers today and scale our distributed footprint for tomorrow.
QumulusAI’s workload-optimized infrastructure gives us the performance, efficiency, and scalability we need as we continue expanding reliable GPU compute for customers building AI at scale.
Jasper Zhang
CEO :: Hyperbolic
Our new AI Lab, powered by QumulusAI infrastructure, gives us the ability to test new ideas quickly and ensure our platform is ready for the next generation of AI workloads. At the same time, customers benefit from enterprise-grade Kubernetes environments optimized for GPU-accelerated development.
Lukas Gentele
CEO :: vCluster
This deployment gives AI inference platforms on Shadeform access to dedicated, enterprise-grade AI infrastructure as they scale their businesses.
Ed Goode
CEO :: ShadeformLatest Articles and News
Frequently Asked Questions About QumulusAI
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QumulusAI works backward from the workload, timeline, region, security requirements, and business model to specify the environment, reserve capacity, deploy and validate the infrastructure, and hand over the production foundation.
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QumulusAI operates the physical infrastructure and contracted managed services, while the customer controls applications, models, data, pipelines, identities, and software configuration unless an additional managed scope is included.
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No; GPU platforms, services, capacity, and timing differ by site and reservation status and are confirmed for the specific project.
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QumulusAI does not publish one universal rate because final pricing depends on the selected infrastructure, capacity, location, reservation term, support scope, and availability. Those items are confirmed in a project-specific quote and governing agreement.
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Applicable controls, evidence, availability commitments, support terms, and responsibility boundaries are reviewed for the specific service and deployment and documented in the governing agreement.
// DEPLOY AT HYPERSPEED