GPU servers
for the future of AI
Build your infrastructure with the latest generation of accelerators. Optimized for PyTorch, TensorFlow And NVIDIA vGPU.
Certified platforms
Direct deliveries of high-density GPU clusters to Russia with a warranty and technical support.
In which tasks do GPU servers provide the greatest benefit?
Specialized platforms for scenarios requiring parallel processing of large volumes of data.
Artificial intelligence
Training and inference
A platform for training neural networks (LLM, RAG, CV) and processing large data sets. FP32/FP16/INT8 performance and video memory capacity are critical.
VDI & Workstations
Virtual desktops
Remote access to powerful graphics for engineers and designers (CAD, 3D, Omniverse). Smooth and responsive performance in a corporate environment.
3D Rendering
Visualization and video
Acceleration of compositing and high-quality video processing. Tasks that take hours on the CPU are completed significantly faster on the GPU.
Scientific calculations
HPC and Simulation
Financial analytics, fluid dynamics, and simulations, where the workload is efficiently parallelized across thousands of accelerator threads.
Key Parameters When Selecting a GPU Server
Before purchasing GPU servers, it's worth reviewing the following parameters to avoid the most common pitfalls in AI and VDI projects.
Choosing the right platform determines the effectiveness of your investment. We help you balance your configuration to avoid overpaying.
Need a consultation?
Our experts will help you choose the optimal solution for your budget.
Submit a requestType of tasks and accuracy
Depends on whether you need training (FP32/FP16) or inference (INT8) and working with the AI stack.
Number and type of GPUs
From 1-2 to 8 or more GPUs in a single chassis. The connection interface (PCIe, NVLink) is important.
Video memory and bandwidth (PSP)
For training large models and 3D scenes, the amount and speed of video memory (VRAM) are critical.
CPU and RAM balance
Недостаточная мощность процессора или малый объём ОЗУ могут стать «узким местом» системы.
Power and cooling
High-density servers require a rethinking of the data center's rack infrastructure.
Network and scaling
Clusters require 25/40/100G connections and a well-designed network architecture.
Software and licensing
Checking support for GPU virtualization (vGPU) frameworks and licenses.
Typical GPU server configurations
Sample specifications based on current NVIDIA accelerators. We'll find the exact equivalent to fit your budget.
Entry level
Pilot projects, inference of small LLMs, CV tasks.
Suitable for teams just launching their first AI-powered services.
Optimal balance
20–40 workstations, 3D design (Omniverse), rendering.
For design studios, engineering departments and media teams.
H100/A100 Clusters
LLM training, generative models, Sora-like videos.
For teams that need to regularly train or retrain their own models.
We support an ecosystem of leading GPU server manufacturers
In our projects, we use server platforms and components from global technology leaders, guaranteeing compatibility with modern AI frameworks and virtualization software.
GPUs and accelerators
NVIDIA
AI industry leader
A100, H100, L40S accelerators and other solutions for LLM training, VDI and professional visualization.
AMD
High parallelism
The Instinct family of accelerators for high-performance computing (HPC) and deep data analytics.
Server platforms and chassis
Dell Technologies
Proven PowerEdge server platforms for hosting GPU workloads in enterprise data centers.
HPE
High-density ProLiant solutions for hybrid infrastructures with support for the latest GPUs.
Lenovo
ThinkSystem systems for AI workloads with a focus on scalability and energy efficiency.
Supermicro
Specialized chassis for maximum density of accelerator placement in a rack.
Specific brands and models are selected for each individual task, taking into account performance requirements, compatibility with your software, and current logistics conditions in Russia.
If specific models are in short supply, we offer equivalent options in terms of performance and resource capacity, coordinating the replacement with your technical team.
How Elish Tech Helps GPU Projects in Russia
We specialize in supplying complex equipment (H100, A100, L40S) to system integrators, corporations, and data centers. Our goal is to address shortages and provide warranty support.
Pre-project consultation
Analysis of tasks (LLM, VDI, Rendering), current infrastructure and software to select optimal H100/L40S options.
Selection of alternatives
In the context of shortages of certain series, we offer solutions equivalent in terms of PSP and VRAM (for example, L40S instead of A100).
Delivery to Russia (2024-2025)
Proven logistics channels. Proven experience importing H100 and A100 clusters into Russia under current restrictions.
Experience in critical systems
We work with banks, data centers, and the public sector. We offer full support, from specifications to commissioning of GPU workloads.
You can send us your current specifications or technical requirements, and we will offer configurations based on available GPUs and platforms to fit your budget.
Submit current specification →
Common Mistakes When Launching GPU Projects
Something that could lead to accidents, downtime and loss of investment.
Gaming GPUs in data centers
Consumer cards are not designed for 24/7 operation and have limitations in cooling and server driver support.
Cooling and power supply
A single GPU server can consume as much power as several racks of legacy hardware—the infrastructure must be ready.
Imbalance of components
A weak CPU or insufficient RAM limits the performance of expensive GPUs, dramatically reducing the ROI of a project.
Ignoring licenses
Lack of vGPU licenses or support for the required versions of AI frameworks (PyTorch/TensorFlow) may disrupt implementation timelines.
Frequently Asked Questions
Technical details and tips for choosing a GPU infrastructure.
Ready to discuss your project?
Send us your specifications or requirements. We will offer configuration options taking into account supply constraints and your AI/LLM requirements.