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Introducing
QumulusAI Cloud

QumulusAI Cloud delivers access to shared and dedicated GPUs — as well as bare metal clusters — enabling revolutionary HPC with unprecedented performance, elastic scaling, transparent pricing, and turnkey infrastructure.

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Shared GPUs

Flexible GPU access for inference, prototyping, and fine-tuning—delivering significant cost savings over traditional clouds.

  • Subscription-based GPU pool

  • Scale in single-GPU increments

  • On-demand or reserved options

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Dedicated GPUs

Dedicated compute for training and advanced workloads with guaranteed access and flexible scaling.

  • Guaranteed 1:1 GPUs

  • Choose 2, 4, or 8 GPU increments

  • On-demand or reserved options

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Bare Metal Clusters

Maximize performance and control by eliminating the hypervisor layer for large-scale model training and fine-tuning.

  • Exclusive 1:1 nodes

  • Deploy in single or multi-node increments

  • Reservations beginning at one month

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The AI Cloud for Any and Every Workload

Large Model
Training

Train large language models and generative models using clusters of high‑memory GPUs

Fine
Tuning

Customize open‑source models with minimal setup

Fast Inference

Deploy low‑latency prediction endpoints at scale

HPC & Simulation

Run compute‑intensive scientific workloads, simulations or rendering jobs

Let's talk tech specs.

With QumulusAI, You Get

  • Bare Metal NVIDIA Server Access (Including H200)

  • Priority Access to Next-Gen GPUs as They Release

  • 2x AMD EPYC or Intel Xeon CPUs Per Node

  • Up to 3072 GB RAM and 30 TB All-NVMe Storage

  • Predictable Reserved Pricing with No Hidden Fees

  • Included Expert Support from Day One

  • 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

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  • 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

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  • 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

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  • 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

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  • 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

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  • GPUs Per Server: 8
    vRAM/GPU: 24 GB
    CPU Type: 2x AMD EPYC 9374F or 2x AMD EPYC 9174F
    CPU Speed: 3.85 GHz (base) / 4.3 GHz (boost)
    vCPUs: 128 or 64
    RAM: 768 GB or 348 GB
    Storage: 15.36 TB or 1.28 TB

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  • GPU Types: A5000, 4000 Ada, and A4000
    GPUs Per Server: 4-10
    vRAM/GPU: 16-24
    CPU Type: Varies (16-24 Cores)
    vCPUs: 40-64
    RAM: 128 GB - 512 GB
    Storage: 1.8 TB - 7.68 TB

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  • GPUs Per Server: 8
    vRAM/GPU: 16 GB
    CPU Type: Varies (16-24 Cores)
    vCPUs: 64
    RAM: 256 GB
    Storage: 3.84TB

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Shared or Dedicated?

Choosing between QumulusAI Cloud and QumulusAI Cloud Pro is about the right-sized compute at the right price for the right job.

Leverage Cost-Efficiency with Shared GPU Access

  1. Fractional access to GPUs for smaller jobs—pay for what you use, not the whole card

  2. Ideal for development, inference, and bursty workloads that don’t fully utilize a GPU

  3. Occasional interruptions are possible, but cost savings are significant

  4. Great for experimentation, prototyping, and workloads with short runtimes

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Get Dedicated GPUs for Mission Critical Jobs

  1. Reserved, non-fractional GPU instances, available in increments of 2, 4, and 8 GPUs.

  2. Consistent, uninterrupted performance for large-scale training or production inference.

  3. Full control of the GPU with guaranteed availability.

  4. Best choice for mission-critical workloads that demand stability and predictability.

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Streamlined AI Deployment Regardless Which Option You Choose

PRE-CONFIGURED FOR ML

GPU instances come with PyTorch, TensorFlow, JAX, Keras, CUDA, and NVIDIA tools.

ONE-CLICK JUPYTER

Instantly launch Jupyter notebooks in your browser with no setup required.

CONTAINER SIMPLICITY

Deploy and scale apps easily using managed container infrastructure built for your stack.

All with Additional Resources Available

HIGH-PERFORMANCE STORAGE

Tiered NVMe storage systems deliver multiple GB/s of throughput per node, with capacity options from terabytes to petabytes.

ULTRA-FAST
NETWORKING

InfiniBand networks provide up to 3.2 Tb/s RDMA interconnects and optional 100 GbE links for mixed workloads.

Or Go Bare Metal with QumulusAI Pure

Maximize Control in a Private, Isolated Environment

  1. Run workloads directly on the hardware with no virtualization layer. This ensures maximum throughput, full GPU power, and predictable performance for intensive AI and HPC tasks.

  2. Leverage high-speed interconnects tuned for distributed training, large-scale inference, and other workloads where every microsecond matters.

  3. Access local NVMe with extreme read/write speeds, enabling faster data pipelines, larger batch sizes, and reduced I/O bottlenecks.

  4. Maintain direct oversight of the cluster environment, giving you the ability to fine-tune performance, configure to your exact needs, and scale without hidden constraints.

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