AI Workstations in Estonia
Single-GPU 48GB inference, model serving, and large system-RAM workloads
Profile: High-Memory Team Server
Honest visual overview
A quick summary of the main AI buying decisions: GPU memory, system RAM, model target, and power class.
This is a schematic summary, not a photo of the exact build.
AI capability fit
Very strong
Workstation tier for larger models and multiple workflows
GPU
NVIDIA RTX 6000 Ada
VRAM
48GB
RAM
512GB
Model target
70B-class single-GPU inference
CPU
AMD Threadripper PRO 7975WX
GPU
NVIDIA RTX 6000 Ada
VRAM
48GB
RAM
512GB
Storage
8000GB
Model target
70B-class single-GPU inference
Throughput
Varies by model/runtime
System power
~850W
Recommended PSU
1600W
Cooling
High-memory workstation cooling with 420mm AIO
70B needs serious memory tradeoffs
70B-class models depend heavily on VRAM/RAM, quantization, and context length.
AI capability fit
Very strong
Workstation tier for larger models and multiple workflows
Based on VRAM and system RAM; quantization, context, and runtime can change the result.
Estimated build market price history
The chart includes all listed components. Observed prices are preferred; missing points are filled with conservative estimates from current prices and category trends. This does not include the assembly markup.
Latest estimated market total
€23,645
5/8 observed · 3 estimated
Not enough trusted price history yet.
This range has 28.5% average trusted value coverage and 48.4% latest coverage. The line chart appears once trusted value coverage reaches at least 60%.
The summary below is a directional market-planning estimate, not the checkout/order price and not a daily scraped history.
Latest estimated market total
€23,645
5/8 observed · 3 estimated · 0 unknown
CPU: AMD Threadripper PRO 7975WX
CPU fallback: current market/reference anchor with a conservative 6% twelve-month decline toward today.
Confidence: low
GPU: NVIDIA RTX 6000 Ada
GPU fallback: current market/reference anchor with a conservative 8% twelve-month decline toward today.
Confidence: medium
RAM: Kingston Server Premier 512GB (8x64GB) DDR5-4800 ECC RDIMM
Memory fallback: current market/reference anchor with a conservative 5% twelve-month category adjustment.
Confidence: low
Storage: WD Black SN850X 8TB
Storage fallback: current market/reference anchor with a conservative 5% twelve-month category adjustment.
Confidence: medium
Motherboard: ASUS Pro WS WRX90E-SAGE SE
Motherboard fallback: current market/reference anchor with a conservative 4% twelve-month decline toward today.
Confidence: low
PSU: be quiet! Dark Power Pro 13 1600W
PSU fallback: current market/reference anchor with a very small 2% twelve-month category adjustment.
Confidence: medium
Case: Phanteks Enthoo Pro 2
Case fallback: flat estimate from the current market/reference anchor.
Confidence: medium
Cooler: Arctic Liquid Freezer III 420
Cooler fallback: flat estimate from the current market/reference anchor.
Confidence: medium
Prices use Estonian market data when available, otherwise reference estimates. Displayed component prices include the assembly/configuration markup; payable order price applies only when the purchase panel allows online checkout.
| Component | Product | Displayed price |
|---|---|---|
| CPU | AMD Threadripper PRO 7975WX Planning reference price | €4,484 |
| GPU | NVIDIA RTX 6000 Ada Updated todayLow price sample | €11,901 |
| RAM | Kingston Server Premier 512GB (8x64GB) DDR5-4800 ECC RDIMM Planning reference price | €8,049 |
| Storage | WD Black SN850X 8TB Updated todayLow price sample | €435 |
| Motherboard | ASUS Pro WS WRX90E-SAGE SE Planning reference price | €1,494 |
| PSU | be quiet! Dark Power Pro 13 1600W Updated todayVerified pricing input | €435 |
| Case | Phanteks Enthoo Pro 2 Updated todayVerified pricing input | €184 |
| Cooler | Arctic Liquid Freezer III 420 Updated 2 days agoLow price sample | €211 |
| Estimated build configuration total | €27,193 | |
High-memory team server built around one RTX 6000 Ada. Custom multi-GPU systems require a separate quote because this catalog schema prices one GPU per build and should not imply two cards are included.
Source refs: nvidia.com, amd.com
Quote reference price
€27,193
Shown for planning. Direct checkout remains quote-only until fresh market pricing and availability are checked.
Price chart shows Estonian market averages before assembly/configuration markup; quote-only pricing is manually confirmed before payment.
Quote-only because this item requires human review.
High-ticket, used/refurbished, pro, datacenter, and Apple compact systems require a manual quote before payment is opened.
What happens after your quote request
Support and questions continue through the order or quote email thread.
Request a verified quote
The request does not take payment. We manually verify price and availability, then confirm substitutions or changes before offering any payment link.
Practical model fit
Examples for Single-GPU RTX 6000 Ada Team Server, based mainly on GPU VRAM and system memory.
Starter pick
A practical first coding assistant for most LLMLab desktop builds.
It is small enough for mainstream GPUs but tuned specifically for code.
Likely good memory headroom for this quantized model at normal context sizes.
Agentic coding experiments and repository-scale prompts
A newer coding model for enthusiasts who want more capable coding behavior on high-VRAM machines.
Likely good memory headroom for this quantized model at normal context sizes.
Heavier coding help and code reasoning on 24GB+ GPUs
A more capable coding model for 24GB and 32GB+ systems, but not the first model a beginner should try.
Likely good memory headroom for this quantized model at normal context sizes.
First local chat, prompt experiments, short summaries
A small, friendly starter model for learning local AI without needing a large GPU.
Likely good memory headroom for this quantized model at normal context sizes.
Light chat, multilingual prompts, compact reasoning tests
A compact Qwen model that gives beginners a taste of newer reasoning-style local models.
Likely good memory headroom for this quantized model at normal context sizes.
Assumptions
Family: Qwen2.5-Coder
Parameters: 7B
Quantization: Q4_K_M
Approx. model size: 4.68GB
CPU-only: Not recommended
VRAM: 8GB min / 12GB recommended
RAM: 16GB min / 32GB recommended
Full GPU offload: Should be possible when memory fits
Context warning: Large files and many open tabs can push memory use above the model size.
Research sources
Researched: 2026-06-22
Family: Qwen3-Coder
Parameters: 30.5B
Quantization: Q4_K_M
Approx. model size: 19GB
CPU-only: Not recommended
VRAM: 24GB min / 32GB recommended
RAM: 64GB min / 96GB recommended
Full GPU offload: Should be possible when memory fits
Context warning: Repository-scale context can require far more memory than a short coding chat.
Research sources
Researched: 2026-06-22
Family: Qwen2.5-Coder
Parameters: 32B
Quantization: Q4_K_M
Approx. model size: 20GB
CPU-only: Not recommended
VRAM: 24GB min / 32GB recommended
RAM: 64GB min / 96GB recommended
Full GPU offload: Should be possible when memory fits
Context warning: 32K context can exceed comfortable memory on 24GB cards.
Research sources
Researched: 2026-06-22
Family: Meta Llama 3.2
Parameters: 3B
Quantization: Q4_K_M
Approx. model size: 2GB
CPU-only: Possible
VRAM: 0GB min / 4GB recommended
RAM: 8GB min / 16GB recommended
Full GPU offload: Should be possible when memory fits
Context warning: Long documents can still push memory use up, even with a small model.
Research sources
Researched: 2026-06-22
Family: Qwen3
Parameters: 4B
Quantization: Q4_K_M
Approx. model size: 2.5GB
CPU-only: Possible
VRAM: 4GB min / 6GB recommended
RAM: 8GB min / 16GB recommended
Full GPU offload: Should be possible when memory fits
Context warning: Keep the context window modest on 8GB to 16GB systems.
Local AI performance is approximate. Results depend on quantization, context length, backend, drivers, RAM, and whether the model fits fully in VRAM.
Order and handover
After payment or quote request
You receive a confirmation email. We check availability and confirm any practical substitutions before continuing.
Assembly and QA
Planned work includes software, drivers, a GPU/AI smoke test, thermal load sanity check, and memory/storage health checks.
Handover in Estonia
Pickup or local delivery method and timing are agreed after availability is checked.
Warranty and support
Warranty depends on the component, manufacturer, and retailer. Questions continue through the order or quote email thread.
Changes and cancellations
Changes are confirmed in writing; after sourcing or assembly begins, custom-order handling may depend on order state.
Payment security
Card details are entered in Stripe checkout. LLMLab.ee does not collect or store full card numbers.
AI terms in plain language
VRAM
Memory on the graphics card; usually the main limit for local AI model size.
Unified memory
Apple Silicon memory shared by CPU and GPU. Useful for local AI, but not identical to NVIDIA VRAM.
7B / 13B / 70B
A rough model-size signal. Larger numbers usually need more memory and may run slower.
q4 / quantization
A compressed 4-bit model that uses less memory, sometimes with quality or speed tradeoffs.