Qwen3 4B
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.
LLMLab.ee
AI Workstations in Estonia
Experimental
Mac mini M4 + Radeon PRO W7900 eGPU (ROCm)
Experimental Setup
This setup uses experimental drivers and software. Not suitable for production use.
Mac System
Mac mini M4 24GB / 512GB
Chip: Apple M4
Unified Memory: 24GB
Storage: 512GB
Mac price: €1199
eGPU Enclosure
Sonnet Breakaway Box 750ex
Enclosure price: €349
GPU
AMD Radeon PRO W7900
VRAM: 48GB
Architecture: RDNA 3
Best for 7B/8B models
Good starting point for chat and coding assistants; larger models need more memory.
This is buyer guidance only. Mac + eGPU fit depends on drivers, runtime, and how much of the model fits in GPU VRAM.
Supported Workloads
- Local LLM inference via ROCm
- tinygrad experiments
- AMD RDNA3+ AI workloads
Not Supported
- Gaming acceleration
- macOS display acceleration
- CUDA workloads
- Final Cut acceleration
Buyer Warning
For AI compute only via ROCm/tinygrad. AMD eGPU support is less mature than NVIDIA CUDA.
This flow is for fit review, not immediate payment. Driver and software risks are reviewed before any possible order is confirmed.
Component Pricing
Mac (Mac mini M4 24GB / 512GB): €1199
Enclosure (Sonnet Breakaway Box 750ex): €349
GPU (AMD Radeon PRO W7900): depends on selection
Component market prices change daily. This is a reference estimate; no payment is taken from this form, and payment only follows an agreed custom quote.
Notes
AMD workstation path using Radeon PRO W7900 (48GB VRAM). Uses a 2-slot GPU that fits the listed enclosure; ROCm on external GPU is experimental.
Practical model fit
Local AI examples
Examples for Mac mini M4 + Radeon PRO W7900 eGPU (ROCm), 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
Llama 3.2 3B Instruct
A small, friendly starter model for learning local AI without needing a large GPU.
It is easy to download, small enough for almost any LLMLab machine, and useful for basic private chat.
Good fit
ollama run llama3.2
Likely good memory headroom for this quantized model at normal context sizes.
Good fit
Mistral 7B Instruct v0.3
Fast general chat and simple assistant tasks
A fast classic 7B model that is easy to run and compare against newer models.
GPU memory should fit well, but longer context can still add pressure.
Llama 3.1 8B Instruct
Everyday private chat and document summaries
A widely supported everyday local chat model when the machine has at least an 8GB to 12GB GPU.
GPU memory should fit well, but longer context can still add pressure.
Qwen3 8B
General chat, analysis, multilingual questions
A capable modern 8B-class local model for chat, analysis, and lightweight reasoning.
GPU memory should fit well, but longer context can still add pressure.
Expandable technical details
Assumptions
- GPU VRAM assumption: 48GB from AMD Radeon PRO W7900.
- System RAM: 24GB.
- Mac + eGPU fit is experimental and depends on driver/runtime support, not just VRAM.
- Ratings assume Q4-style quantization, moderate context, one local model running at a time. Treat them as fit guidance, not a speed estimate.
- This setup depends on experimental Mac + external GPU runtime support. Treat fit ratings as a pre-quote discussion starter.
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
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.
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
Researched: 2026-06-22
Llama 3.1 8B Instruct technical details
Family: Meta Llama 3.1
Parameters: 8B
Quantization: Q4_K_M
Approx. model size: 4.9GB
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: The Q4 model is under 5GB, but KV cache grows with context length.
Research sources
Researched: 2026-06-22
Qwen3 8B technical details
Family: Qwen3
Parameters: 8B
Quantization: Q4_K_M
Approx. model size: 5.03GB
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: A 32K+ context can be much heavier than a short chat session.
Local AI performance is approximate. Results depend on quantization, context length, backend, drivers, RAM, and whether the model fits fully in VRAM.
Trust details
Important before ordering
Contact and support
Questions continue through the order or quote email thread. Replying to the confirmation is the fastest path.
Warranty
Warranty handling depends on the component, manufacturer, and retailer; the practical path is confirmed case by case.
Handover in Estonia
Pickup or local delivery method and timing are agreed after availability is checked.
Cancellations and changes
Cancellations and changes are confirmed in writing through the quote or order thread; 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.
Pricing method
We show the Estonian market average before assembly and the order price with the 15% assembly and configuration markup.