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AI Workstations in Estonia

FAQ

Tuning Stability

LLM Fine-Tune Starter

Platforms with enough system RAM and stable cooling for LoRA adapters and custom training runs.

Best for

  • LoRA and QLoRA adapter training
  • Longer sustained workloads with stable cooling
  • More system RAM for datasets and tooling

Not ideal for

  • Overbuilt if you only want local chat
  • Full model training still needs much larger hardware
  • Costs more than inference-first systems

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

Budget Fine-Tune Entry

Entry point for learning fine-tuning, embeddings, RAG pipelines, and small batch experiments. The 16GB VRAM is useful, but the narrow memory bus makes it a budget learning machine rather than a high-throughput trainer.

GPU: NVIDIA RTX 4060 Ti 16GB

CPU: AMD Ryzen 9 7900

RAM: 64GB | Storage: 2000GB

Target: 7B LoRA / embeddings

Good for 13B-class models

Strong everyday local LLM tier; 30B may need more memory or heavier quantization.

Good for everyday local LLM use

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

€2,081

6 market-priced parts, 2 reference estimates

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CUDA Adapter-Tuning Starter

Starter tuning build for LoRA and QLoRA experiments where CUDA compatibility matters. The 96GB RAM leaves room for datasets, loaders, and dev tooling while the 16GB GPU keeps runs realistic for 7B-13B models.

GPU: NVIDIA RTX 4070 Ti SUPER

CPU: AMD Ryzen 9 7950X

RAM: 96GB | Storage: 2000GB

Target: 7B-13B LoRA

Good for 13B-class models

Strong everyday local LLM tier; 30B may need more memory or heavier quantization.

Good for everyday local LLM use

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

€3,617

6 market-priced parts, 2 reference estimates

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16GB VRAM Fine-Tune Workhorse

More serious 7B-13B LoRA/QLoRA workstation with CUDA, 96GB RAM, and 4TB fast storage for datasets and checkpoints. It is still a single 16GB GPU, so training plans should stay adapter-based.

GPU: NVIDIA RTX 4070 Ti SUPER

CPU: AMD Ryzen 9 9950X

RAM: 96GB | Storage: 4000GB

Target: 7B-13B LoRA/QLoRA

Good for 13B-class models

Strong everyday local LLM tier; 30B may need more memory or heavier quantization.

Good for everyday local LLM use

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

€3,486

5 market-priced parts, 3 reference estimates

View details