NVIDIA and AMD GPU platforms — from compact inference nodes to HGX-class training systems. Entry inference from $8,900.
Modern AI workloads depend on GPU memory, interconnect, storage, networking, cooling and software compatibility. We help customers choose the right configuration for their model size, workload type, latency target, number of users and data security requirements.
For companies that want to run language models on their own infrastructure.
For search, tender analysis, legal review, knowledge bases and enterprise assistants.
For teams adapting open-source or proprietary models to specific business tasks.
For labs, universities and engineering teams requiring multi-GPU platforms.
NVIDIA and AMD platforms validated for inference, fine-tuning, training and HPC workloads — with networking, power and cooling planned together.
Three tiers of validated configurations. Pick the class that matches your workload — we configure the rest.
Compact to mid-range GPU servers for private LLMs, RAG and computer vision.
A compact and energy-efficient inference platform for entry-level AI workloads, computer vision and document automation.
A practical server class for companies that need large GPU memory for private LLMs without moving immediately to HGX-class systems.
A balanced platform for teams that need more GPU capacity than entry inference servers, but do not require H100/H200-class systems yet.
Multi-GPU H100 platforms for fine-tuning and serious ML research.
A professional training-class system for serious AI teams that require H100 performance, multi-GPU scaling and enterprise support.
Rack-scale H200 / B200 / MI300X platforms for large-LLM and HPC workloads.
A high-end HGX-class platform for memory-intensive AI workloads where HBM capacity and interconnect performance matter.
A next-generation Blackwell-class platform for customers planning serious AI infrastructure at data-center scale.
A high-memory AI platform for teams evaluating AMD Instinct accelerators for large model workloads. Software stack and model compatibility are validated per project.
All prices are indicative and exclude VAT, customs duties, special logistics, installation and country-specific certification unless stated otherwise. Final pricing depends on configuration, GPU availability, warranty terms, Incoterms and destination.
Instead of selling a random GPU box, we map the workload first and then recommend the correct platform: PCIe GPU server, SXM/HGX node, AMD high-memory system or rack-scale cluster.
Private knowledge base, document search, legal/tender analysis and internal assistants.
Local inference for organizations that cannot send sensitive data to public APIs.
Object detection, security analytics, industrial vision and edge AI.
Adaptation of open-source models to corporate datasets and specialized workflows.
Multi-GPU systems for universities, labs and engineering teams.
GPU servers supplied for system integrators, SaaS teams and enterprise AI solution providers.
We help select hardware based on the actual AI task, not just GPU availability.
We use clear product classes, correct GPU memory figures and transparent configuration logic.
NVIDIA, AMD, Supermicro, Dell, HPE, Lenovo and compatible OEM platforms depending on the project.
We support international sourcing, documentation, export compliance checks and destination-specific delivery planning.
From one inference node to multi-node GPU clusters with high-speed networking.
Each proposal can include bill of materials, system configuration, power/cooling requirements and warranty conditions.
Enterprise AI infrastructure depends on fast-changing GPU availability, export rules, OEM allocation, destination country and warranty coverage. We do not promise unrealistic delivery dates, fixed global prices or unsupported configurations.
A short reference for business buyers and procurement teams.
For most companies, the right starting point is an inference or RAG server, not an H100/H200 training cluster. The correct choice depends on model size, number of users, data security and latency requirements.
Yes, but the required hardware depends on the model size, quantization, context length and number of concurrent users. Smaller models can run on inference-class servers, while larger models require high-memory GPUs or multi-GPU platforms.
The cost is driven by GPU memory, HBM bandwidth, interconnect, OEM platform, high-speed networking, power, cooling, warranty and limited availability.
Yes, but training from scratch requires a much larger budget than inference or fine-tuning. We recommend starting with a workload assessment.
Yes, AMD MI300X/MI325X-class platforms can be considered for memory-heavy workloads. Software compatibility is validated per project.
No. The prices on the website are indicative. Final pricing depends on configuration, availability, logistics, warranty and destination.
We can provide configuration documentation, delivery support, and integration planning. On-site services depend on country and partner availability.
Send us your workload, model size or target use case. We will recommend a realistic configuration and provide an indicative price range — typically within 1 business day.
COMTRADE