LLMLab.ee

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 this a learning machine rather than a high-throughput trainer.

GPU: NVIDIA RTX 4060 Ti 16GB

CPU: AMD Ryzen 5 7600

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

€2,340

5 market-priced parts, 3 reference estimates

View details

CUDA Adapter-Tuning Starter

Starter tuning build for LoRA and QLoRA experiments where CUDA compatibility matters. Budget is kept on GPU, RAM, and storage instead of an oversized motherboard; the 16GB GPU still keeps training plans adapter-based.

GPU: NVIDIA RTX 4070 Ti SUPER

CPU: AMD Ryzen 9 7900

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

€3,496

5 market-priced parts, 3 reference estimates

View details

16GB VRAM Fine-Tune Workhorse

Serious 7B-13B LoRA/QLoRA workstation with CUDA, 96GB RAM, and 4TB fast storage for datasets and checkpoints. The board is sized for reliability rather than prestige; the single 16GB GPU keeps training plans 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

€3,975

4 market-priced parts, 4 reference estimates

View details

Practical model fit

Local AI examples

Examples for LLM Fine-Tune Starter 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.

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.

Qwen3 4B

Light chat, multilingual prompts, compact reasoning tests

Good fit

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.

Mistral 7B Instruct v0.3

Fast general chat and simple assistant tasks

Good fit

A fast classic 7B model that is easy to run and compare against newer models.

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

Expandable technical details

Assumptions

  • GPU VRAM assumption: 16GB from NVIDIA RTX 4060 Ti 16GB.
  • System RAM: 64GB.
  • 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-CoderQwen2.5-Coder 7B GGUF sizesQwen2.5-Coder collection

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 3.2Meta Llama 3.2 3B Instruct model cardOllama Llama 3.2 3B Q4 tag

Researched: 2026-06-22

Qwen3 4B technical details

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.

Research sources

Ollama Qwen3 4BQwen3 4B model card

Researched: 2026-06-22

Mistral 7B Instruct v0.3 technical details

Family: Mistral 7B

Parameters: 7.3B

Quantization: Q4_K_M

Approx. model size: 4.4GB

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: Long context support does not mean every machine should use the maximum context.

Research sources

Mistral 7B announcementMistral 7B Instruct v0.3 model cardOllama Mistral 7B Instruct 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.