GPU & AI DEDICATED SERVERS
Choose single or dual GPU configurations for your compute workload. Compare GPU memory with CPU, system RAM and storage, then match the software stack to the hardware.
01 / THE SHORT ANSWER
A GPU dedicated server provides physical GPU resources for AI and machine-learning workloads. The four configurations listed here use one GTX 1080 Ti, two GTX 1080 Ti, two RTX 3080 or two RTX 3080 Ti GPUs, with CPU, system RAM, NVMe storage and network resources. Compare per-device memory and software compatibility before choosing.
02 / CHOOSE THE GPU
| GPU configuration | GPU VRAM | System memory | Storage | Workload considerations |
|---|---|---|---|---|
| 1 × GTX 1080 Ti | 11GB GDDR5X | 32GB DDR4 | 240GB NVMe | A single GPU for workloads that fit its device memory and a compatible software stack. |
| 2 × GTX 1080 Ti | 22GB GDDR5X total | 128GB DDR4 | 240GB NVMe | Separate jobs or software-directed parallel work across two devices. |
| 2 × RTX 3080 | 20GB GDDR6X total | 128GB DDR4 | 240GB NVMe | Consider Tensor-capable compute where the application and per-device memory fit. |
| 2 × RTX 3080 Ti | 24GB GDDR6X total | 128GB DDR4 | 240GB NVMe | Compare the larger published memory total with your application’s device-level requirements. |
GPU memory and system RAM serve different roles. The listed memory totals on dual-GPU configurations are aggregate capacity across two devices; applications do not automatically receive one pooled memory bank.
Compare infrastructure types ↗03 / AVAILABLE GPU SERVERS
* Stock dependent.
Dual-GPU VRAM figures are aggregate totals across two devices. Traffic is unmetered; port speed is shown per configuration. Each configuration includes one IPv4 address.
04 / VRAM & MODEL FIT
Model weights, working tensors and batch or context data all need working space. Training can also require gradients and optimizer state; inference memory grows with the model and the work being served.
Start with the memory available to each GPU. Two devices can hold separate work, but distributing a model across them requires application support. More aggregate VRAM is not an automatic answer to a single-device memory limit.
No fixed model-size promise: precision, application, batch size and context all affect the memory requirement.
05 / TRAINING VS INFERENCE
| Requirement | Training | Inference |
|---|---|---|
| Compute pattern | Repeated batches update model parameters. | Requests use a trained model to produce results. |
| Working memory | Weights, activations, gradients and optimizer state. | Model weights, request context and concurrent work. |
| Data movement | Dataset reads and periodic checkpoint writes. | Request inputs, model loading and result delivery. |
| Review first | Batch size, compatible training stack and parallel strategy. | Model memory, batching, context and concurrency. |
Resource demands vary by application. Neither GPU count nor memory alone guarantees training time, throughput or response latency.
06 / SINGLE GPU VS MULTI-GPU
A single GPU keeps an AI/ML job and its working data within one device. Multiple GPUs can run independent AI/ML jobs or share an application that supports distributed work.
Partitioning, synchronization and data movement affect the result. Check the application’s parallel strategy and memory requirements before choosing a dual-GPU configuration. Two devices do not guarantee twice the speed.
The illustration shows software-directed parallel work. It does not specify a hardware interconnect.
07 / DATA, STORAGE & CPU SUPPORT
08 / GPU SOFTWARE & CONTROL
Customers can deploy the software stack appropriate to their selected operating environment and GPU workload. Match the GPU generation, driver, toolkit and application requirements before provisioning.
Pascal compatibility needs particular care: CUDA 13 removed offline compilation for architectures before compute capability 7.5. Do not assume every current framework build supports GTX 1080 Ti.
09 / GPU WORKLOAD FIT
Evaluate model weights, precision and context against per-device VRAM. Check the software stack before planning self-hosted generation.
Match batches, gradient and optimizer memory with dataset and checkpoint I/O. Parallel training needs a supported workload design.
Consider model residency and request concurrency. Validate your model and batch strategy rather than assuming a response rate.
Image resolution, augmentation and batch size shape the workload. CPU preparation and GPU memory both affect the fit.
Plan preprocessing and data loading alongside training or inference. Check CPU, system RAM and storage requirements so the selected GPU has the data it needs.
Measure your AI/ML application’s memory use and execution behavior before choosing a configuration. Hardware specifications alone do not establish model fit or throughput.
10 / FAQ
A physical GPU dedicated server combines one or more GPUs with CPU, system RAM, storage and network resources for AI/ML workloads. Choose the GPU and the supporting configuration together.
The four public configurations use a single GTX 1080 Ti, two GTX 1080 Ti, two RTX 3080 or two RTX 3080 Ti GPUs. Deployment depends on hardware availability.
VRAM is memory local to a GPU. Model data, working tensors and batches need space there. Memory totals across multiple GPUs are not automatically one memory bank; distributing work requires application support.
Consider multiple devices for independent jobs or applications with a supported parallel strategy. Check per-device memory, software and data movement. Two GPUs do not guarantee twice the speed.
Training updates model parameters using data. Inference uses a trained model to produce results. Memory and compute needs depend on the model, batch size, context and concurrency.
Framework preinstallation is not confirmed for these packages. Agree the operating environment and any driver or framework provisioning separately before deployment.
Customers can deploy the software stack appropriate to their selected operating environment and GPU workload. Check hardware compatibility and agree any specific provisioning requirements before deployment.
No. GTX 1080 Ti is a Pascal GPU without Tensor Cores. RTX 3080 and RTX 3080 Ti have third-generation Tensor Cores. Their use depends on compatible software and operations.
Every current listing specifies 240GB NVMe. Budget space for the operating system, models, datasets and checkpoints. The package details do not establish RAID or additional storage capacity.
Public GPU package conditions estimate 1–2 days depending on hardware availability. The minimum contract is one month; deployment is not instant or guaranteed independently of stock.
YOUR NEXT CONFIGURATION
Review the GPU, memory and complete server configuration against the AI/ML workload you want to run.
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