AI Inference
Model size, VRAM, concurrency, target latency
Describe workload →GPU SERVERS
GPU Servers in Georgia
Monthly dedicated GPU infrastructure for AI inference, rendering, machine learning and compute workloads.
QUICK ANSWER
In brief
GPU servers in Georgia are dedicated GPU compute systems for AI inference, machine learning, rendering, video processing and other GPU workloads. Configuration is selected around VRAM, GPU count, CPU, RAM, storage and software requirements.
GPU BY WORKLOAD
The right GPU starts with VRAM, concurrency and software stack
The model name matters, but it does not guarantee fit on its own. We review GPU, CPU, RAM, storage and sustained monthly usage as one system.
Machine Learning
Framework, dataset, VRAM, CPU/RAM balance
Describe workload →Rendering
Renderer, scene size, VRAM, multi-GPU benefit
Describe workload →Video Processing
Codec support, concurrent streams, storage throughput
Describe workload →HOW WE SIZE IT
Five inputs that materially change a GPU quote
If you only know the workload and budget, that is enough to start. We can clarify the rest together.
CURRENT GPU CATALOG
Current models with pricing logic and quote-time availability
Public pricing appears only when the current cost and availability are verified. If pricing is not confirmed yet, the model remains quoteable without inventing a number.
GPU-L40S-1On request
NVIDIA L40S
48 GBVRAM / GPU
- GPU count
- 1
- CPU
- Confirmed with current partner inventory
- RAM
- Confirmed with current partner inventory
- Storage
- Confirmed with current partner inventory
Sustained inference, rendering and mixed AI/graphics workloads that benefit from 48 GB VRAM.
AI inferenceRenderingVideo processingComputer visionGPU compute
MONTHLY$2,520Approx. 6,576 GEL/mo
SETUP$1,51260% one-time
GPU-RTX4000ADACurrent model - price confirmed in quote
NVIDIA RTX 4000 Ada
20 GBVRAM / GPU
- GPU count
- 1
- CPU
- Intel Xeon Gold - 24 cores
- RAM
- 64 GB DDR4
- Storage
- 2 x 1.92 TB SSD
A current Georgia GPU option for rendering, media workflows and moderate inference where 20 GB VRAM is sufficient.
RenderingVideo processingAI inferenceGPU compute
Request current pricePublic pricing is shown only after current cost and availability are verified.
Request this GPUHELP ME CHOOSE A GPU
Start with the workload, not the model name
This sizing assistant gives you a starting VRAM class. It is sizing guidance for the conversation, not an automatic purchase or a guarantee of fit.
DEDICATED GPU VS HOURLY CLOUD
When monthly dedicated GPU makes sense
ScenarioDedicated GPUHourly / burst cloudFit
Sustained 24/7 inferenceStrong fitCan become expensiveCompare monthly economics
Regular rendering workloadGood fitDepends on burst patternReview utilization
Occasional experimentsOften overkillOften more convenientDo not force dedicated
WHAT TO SEND
Better context means fewer irrelevant GPU options
Model / workloadFramework / CUDA requirementsMinimum VRAMExpected concurrencyMonthly usage patternBudget rangeStorage requirementTarget deployment date
BUYER QUESTIONS
Before the GPU quote
Is hourly billing available?
No. ServerGeorgia provides dedicated GPU infrastructure with monthly billing.
Why is there no fixed public GPU inventory?
GPU availability and current commercial pricing change over time. We prefer to confirm the actual model and price at request time rather than display a stale card.
Do I need to know the exact GPU model?
No. Workload, VRAM, framework, concurrency and budget are usually enough to start sizing.
SERVICE SCOPE
Who it fits, what is included and where responsibility stops
Who this is for
- AI inference and machine learning workloads
- Rendering and video processing pipelines
- Teams that need dedicated GPU compute rather than shared acceleration
What is included
- GPU model and count confirmed in the quote
- CPU, RAM and storage sized around the workload
- Network and deployment terms documented before approval
What is not included by default
- A guaranteed GPU model before availability is confirmed
- Model training or application engineering unless separately scoped
- Managed software stack unless agreed
How to order
- Send GPU workload, framework, VRAM and concurrency requirements
- We match available hardware to the workload
- Review price, network and deployment target
- Approve the quote and deployment scope
FREQUENTLY ASKED QUESTIONS
Direct answers before you order
Do you offer GPU servers for AI?
Yes. GPU infrastructure can be quoted for AI inference, machine learning, rendering, video processing and dedicated GPU compute workloads.
How do I choose the GPU?
Start with minimum VRAM, framework, model size, concurrency and expected runtime. The final hardware depends on workload fit and availability.
WHAT TO SEND US
How to get a useful quote without writing a long technical specification
The three inputs below are usually enough to start a useful technical conversation.
Start with the workload
Describe the AI model, inference concurrency, rendering engine, video pipeline or other GPU workload.
Share VRAM and software constraints
If you know minimum VRAM, GPU count, CUDA/framework requirements or container stack, include them.
Add budget and timing
This helps filter out configurations that technically work but make no commercial sense.
AFTER YOU SUBMIT
What happens after you send the request
Review the scope
We check whether the information is sufficient and identify any technical constraints that need clarification.
Shape the solution
We shape the infrastructure or work scope instead of sending a generic price list.
Confirm commercial terms
The quote shows monthly fee, setup fee, contract, timing and important assumptions.
Move to delivery
After approval, ServerGeorgia remains your contact point for the next stage.
Need a configuration for your workload?
Send the technical requirements and we will prepare an infrastructure proposal.