Local LLM Inference

Cheapest 12GB VRAM Build

Lowest-cost sensible CUDA entry for local AI. Good for 7B quantized chat, embeddings, and learning Ollama or llama.cpp; 13B models may require tighter quantization and shorter context.

€1,368

7 market-priced parts, 1 reference estimates

Estonian Value 16GB Build

Value build selected around parts that are easier to source from Estonian retailers. Good first serious local AI machine for 7B-13B models, RAG, and coding assistants without overbuying flagship hardware.

€1,786

6 market-priced parts, 2 reference estimates

AMD Value 16GB Inference

Value-focused AMD inference build with enough VRAM for useful 13B and some 20B quantized work. Best when the target stack supports ROCm; choose NVIDIA instead if CUDA-only libraries are required.

€2,209

5 market-priced parts, 3 reference estimates

RTX 3090 Used Value Build

Used-market value build centered on 24GB of CUDA VRAM. Excellent for 34B quantized models and larger offload experiments, but used GPU condition, thermals, and warranty should be checked carefully.

€3,001

4 market-priced parts, 4 reference estimates

Efficient 20B Workstation

Efficient CUDA build for 7B-20B inference, coding assistants, and private chat without excessive heat or power draw. The 12GB VRAM is the limiter, so choose this when efficiency matters more than large-model headroom.

€2,390

4 market-priced parts, 4 reference estimates

Balanced NVIDIA 16GB

Balanced CUDA choice for local chat, coding assistants, embeddings, and 13B-34B quantized models. It avoids flagship pricing while keeping enough VRAM and system RAM for practical daily AI work.

€2,859

7 market-priced parts, 1 reference estimates

Blackwell 5070 Ti 16GB Build

Latest-generation 16GB NVIDIA option for buyers who want Blackwell features, GDDR7 bandwidth, and CUDA compatibility. Good for 13B-34B quantized inference, but still not a replacement for 24GB+ VRAM builds.

€3,095

7 market-priced parts, 1 reference estimates

24GB VRAM Value (ROCm path)

Strong VRAM-per-euro option for buyers comfortable with the AMD ROCm path. Great for 13B-34B inference, but CUDA-only tools may need alternatives or extra setup work.

€3,188

5 market-priced parts, 3 reference estimates

Power-Efficient RTX 4000 Ada Build

Quiet, efficient always-on inference box with a 20GB professional NVIDIA GPU. Best for homelab serving, private assistants, and low-noise office use where power draw matters more than peak gaming performance.

€2,710

5 market-priced parts, 3 reference estimates

Flagship 24GB CUDA Inference

Best fit for users who want the strongest consumer CUDA box without stepping into pro GPUs. 24GB VRAM handles 13B-34B models comfortably and can run many 70B quantized setups with careful context settings.

€3,643

7 market-priced parts, 1 reference estimates