Match accelerators to the model, data and operating mode.
← Capability overview
BTG SOLUTION
Servers for AI, analytics and LLM workloads
GPU server infrastructure for artificial intelligence (AI) and large language models (LLM): training, fine-tuning and inference with suitable storage and networking.
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01
Customer needs addressed
The architecture is shaped around the customer’s objectives, constraints and operating model.
Address bottlenecks in memory, storage, networking and cooling.
Prepare a scalable environment for engineering and analytics teams.
02
Solution scope
The exact scope is defined after assessment and agreement on project boundaries.
GPU servers and compute nodes sized to model profiles.
High-speed networking and interconnects for data exchange.
Fast storage for datasets, checkpoints and outputs.
Power, cooling, monitoring and the operating software stack.
03
Project stages
Assessment
We collect inputs, requirements and constraints.
Architecture
We prepare the target design, scope and delivery plan.
Pilot
We validate critical assumptions in an agreed environment.
Implementation
We supply, configure, integrate and test.
Handover
We document the result and hand it over for operation.
04
Support and development
After commissioning, BTG can support agreed components and assist with diagnostics, changes, updates and architecture development. Terms, scope and response arrangements are defined separately for each project.
- Financial organizations
- Public sector
- Industry and energy
- Enterprise and distributed organizations
We work on projects in Almaty and other regions of Kazakhstan. Assessment, supply, implementation and on-site formats are agreed with the customer’s locations.
05 / Anonymized example
AI and LLM servers
For an internal inference service, first define model size, concurrent requests, acceptable latency and data-protection requirements. Representative testing then determines accelerator count and scaling design.
The example describes a typical approach and does not disclose customer information.07
Frequently asked questions
Can a server be selected by GPU memory alone?
No. Compute profile, bandwidth, CPU, RAM, storage, networking, power and cooling all matter.
Is a pilot required?
For new models and unusual workloads, a pilot reduces sizing risk.
Can the solution scale?
Yes, when networking, storage, racks, power and cluster management are designed for growth.
BTG / ALMATY / KAZAKHSTAN
Discuss the task with BTG
Describe the current environment, task and expected result. The BTG team will suggest a practical next step without unsupported promises on timing or outcomes.