AI Workstations in Estonia

Quote-Reviewed Heavy AI

AI Workstations & Custom Multi-GPU Quotes

Listed systems are mostly single-GPU workstations; custom multi-GPU and very high-VRAM systems are quote-reviewed before payment.

Best for

70B-class models with quantization/context caveats
Single-GPU catalog systems and custom multi-GPU quotes
ECC RAM, high VRAM, and workstation cooling

Not ideal for

Much higher cost and power draw
Physically larger and louder under load
Unnecessary for basic local chat or small models

AI fit is a rough estimate; model/runtime/quantization affects results.

Workstation / custom quote path

Software Developer AI Workstation

Configured by LLMLab.ee

Developer-first workstation for local models, IDEs, containers, databases, and browser-heavy workflows running at the same time. The 24GB CUDA GPU covers serious inference while 128GB RAM keeps the rest of the workspace smooth.

GPU: NVIDIA RTX 4090

CPU: AMD Ryzen 9 9950X

RAM: 128GB | Storage: 4000GB

Target: 34B q4

Better for 30B-class models

Stronger fit for larger quantized models; actual fit depends on runtime and settings.

Strong for larger quantized models

Roughly suitable for: local coding assistants and 7B/8B models
Roughly suitable for: 13B/14B quantized models

€6,230

5 market-priced parts, 3 reference estimates

View details

Workstation / custom quote path

Radeon PRO W7900 48GB Workstation

Configured by LLMLab.ee

AMD professional workstation path with 48GB VRAM and 256GB ECC RAM for ROCm-targeted workloads. It should be quoted only after runtime compatibility is validated; CUDA-first software should be assumed NVIDIA-first unless tested.

GPU: AMD Radeon PRO W7900

CPU: AMD Threadripper 7970X

RAM: 256GB | Storage: 4000GB

Target: 70B q4 ROCm-targeted; validation required

70B needs serious memory tradeoffs

70B-class models depend heavily on VRAM/RAM, quantization, and context length.

Workstation tier for larger models and multiple workflows

Roughly suitable for: local coding assistants and 7B/8B models
Roughly suitable for: 13B/14B quantized models

€14,192

4 market-priced parts, 4 reference estimates

View details

Workstation / custom quote path

Threadripper 48GB Pro Workstation

Configured by LLMLab.ee

Professional single-GPU workstation for sustained 70B-class inference, large context windows, and heavy multitasking. The 48GB RTX 6000 Ada is the key upgrade: more VRAM, workstation thermals, and better fit for long unattended jobs.

GPU: NVIDIA RTX 6000 Ada

CPU: AMD Threadripper 7960X

RAM: 256GB | Storage: 4000GB

Target: 70B q4 sustained

70B needs serious memory tradeoffs

70B-class models depend heavily on VRAM/RAM, quantization, and context length.

Workstation tier for larger models and multiple workflows

Roughly suitable for: local coding assistants and 7B/8B models
Roughly suitable for: 13B/14B quantized models

€20,423

5 market-priced parts, 3 reference estimates

View details

Workstation / custom quote path

Single-GPU RTX 6000 Ada Team Server

Configured by LLMLab.ee

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.

GPU: NVIDIA RTX 6000 Ada

CPU: AMD Threadripper PRO 7975WX

RAM: 512GB | Storage: 8000GB

Target: 70B-class single-GPU inference

70B needs serious memory tradeoffs

70B-class models depend heavily on VRAM/RAM, quantization, and context length.

Workstation tier for larger models and multiple workflows

Roughly suitable for: local coding assistants and 7B/8B models
Roughly suitable for: 13B/14B quantized models

€26,953

5 market-priced parts, 3 reference estimates

View details

Practical model fit

Local AI examples

Examples for High-Memory Team Server profile example, based mainly on GPU VRAM and system memory.

Good fit for private chatGood fit for coding helpGood fit for document summariesNot ideal for 70B+ models

Starter pick

Qwen2.5-Coder 7B Instruct

A practical first coding assistant for most LLMLab desktop builds.

It is small enough for mainstream GPUs but tuned specifically for code.

Good fit

ollama run qwen2.5-coder:7b

Likely good memory headroom for this quantized model at normal context sizes.

Qwen3-Coder 30B-A3B

Agentic coding experiments and repository-scale prompts

Good fit

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.

Qwen2.5-Coder 32B Instruct

Heavier coding help and code reasoning on 24GB+ GPUs

Good fit

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.

Llama 3.2 3B Instruct

First local chat, prompt experiments, short summaries

Good fit

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.

Expandable technical details

Assumptions

GPU VRAM assumption: 48GB from NVIDIA RTX 6000 Ada.
System RAM: 512GB.
Ratings assume Q4-style quantization, moderate context, one local model running at a time. Treat them as fit guidance, not a speed estimate.
Profile page uses a representative listed build. Open a build detail page for exact component-level fit.

Qwen2.5-Coder 7B Instruct technical details

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

Ollama Qwen2.5-Coder Qwen2.5-Coder 7B GGUF sizes Qwen2.5-Coder collection

Researched: 2026-06-22

Qwen3-Coder 30B-A3B technical details

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

Ollama Qwen3-Coder 30B Qwen3-Coder blog Unsloth Qwen3-Coder local guide

Researched: 2026-06-22

Qwen2.5-Coder 32B Instruct technical details

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

Ollama Qwen2.5-Coder Ollama Qwen2.5-Coder tags Qwen2.5-Coder 32B model card

Researched: 2026-06-22

Llama 3.2 3B Instruct technical details

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

Ollama Llama 3.2 Meta Llama 3.2 3B Instruct model card Ollama Llama 3.2 3B Q4 tag

Researched: 2026-06-22

Local AI performance is approximate. Results depend on quantization, context length, backend, drivers, RAM, and whether the model fits fully in VRAM.