LLMLab.ee

AI Workstations in Estonia

AMD Radeon RX 9070 XT

AMD graphics card

Honest visual overview

Product schematic

A schematic summary of the key facts before reviewing price and fit.

This is a schematic summary, not a photo of the exact build.

Type

gpu

Brand

AMD

MPN / SKU

AMD-RADEON-RX-9070-XT

Purchase mode

Quote review

Details

Brand

AMD

MPN / SKU

AMD-RADEON-RX-9070-XT

Release

2025 Q1

VRAM

16GB GDDR6

Architecture

RDNA 4

Display Power

304W

Connector Standard

12VHPWR

Minimum PSU

750W

Dual GPU Capable

No

Memory Bus

256-bit

Bandwidth

576 GB/s

Stream Processors

4096

Base / Boost Clock

2400 / 2970 MHz

TDP

304W

PCIe Generation

PCIe 4.0

Slot Width

3-slot

Length

337mm

Power Connectors

16-pin 12VHPWR

Recommended PSU

750W

AI Score

79

Source

https://www.amd.com/

Inference Notes

RDNA 4 with 16GB. AI via WMMA. ROCm improving.

Best for 7B/8B models

Good starting point for chat and coding assistants; larger models need more memory.

AI terms in plain language

VRAM

Memory on the graphics card; usually the main limit for local AI model size.

Unified memory

Apple Silicon memory shared by CPU and GPU. Useful for local AI, but not identical to NVIDIA VRAM.

7B / 13B / 70B

A rough model-size signal. Larger numbers usually need more memory and may run slower.

q4 / quantization

A compressed 4-bit model that uses less memory, sometimes with quality or speed tradeoffs.

Estimated Market Pricing

Estonian market reference estimate before assembly. Used to prepare your custom quote.

Updated 3 days agoVerified pricing inputIncludes 15% assembly markup
Quote reference: €670·Latest Estonian market average before assembly: €583·Assembly + configuration: +15%
Low: 544.32High: 593.19Avg: 560.5131 data points
Latest

Pricing & Purchase

Estonian market average before assembly:582.60

Quote reference price:670

Updated 3 days agoVerified pricing input

Shown for planning. Direct checkout remains quote-only until fresh market pricing and availability are checked.

Quote-only because the latest market pricing is stale.

Fresh, non-fallback Estonian market pricing is required before Stripe payment can be opened. Use the verified quote request on this page for manual review.

What happens after your quote request

  • No payment is taken from the quote request form.
  • We review your use case, model targets, timeline, and budget.
  • We verify suitable parts and current Estonian market pricing.
  • Possible substitutions or changes are confirmed before any payment link.
  • We usually send the next step or follow-up questions within 1-2 business days.

Support and questions continue through the order or quote email thread.

Request a verified quote

The request does not take payment. Price, availability, and possible substitutions are confirmed before any payment link.

Quote item: AMD Radeon RX 9070 XT

No payment is taken from this form. Pricing, availability, substitutions, and payment options are confirmed before any checkout link is offered.

What happens after your quote request

  • No payment is taken from the quote request form.
  • We review your use case, model targets, timeline, and budget.
  • We verify suitable parts and current Estonian market pricing.
  • Possible substitutions or changes are confirmed before any payment link.
  • We usually send the next step or follow-up questions within 1-2 business days.

Support and questions continue through the order or quote email thread.

Practical model fit

Local AI examples

Examples for AMD Radeon RX 9070 XT, 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.

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.

Llama 3.1 8B Instruct

Everyday private chat and document summaries

Good fit

A widely supported everyday local chat model when the machine has at least an 8GB to 12GB GPU.

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

Expandable technical details

Assumptions

  • GPU VRAM assumption: 16GB from AMD Radeon RX 9070 XT.
  • Assumed RAM: 32GB.
  • Ratings assume Q4-style quantization, moderate context, one local model running at a time. Treat them as fit guidance, not a speed estimate.
  • GPU product pages assume a sensible amount of system RAM for this VRAM class. Complete build pages show page-specific RAM fit.
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.

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

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.

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.

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

Trust and process

What happens after an order or quote request

After payment

You receive a confirmation email. We then check part availability and contact you if any component may need a practical substitution.

Assembly and testing

The planned workflow is assembly, software setup, and baseline GPU/AI checks before handover.

Handover in Estonia

Pickup or local delivery method and timing are agreed after availability is checked.

Warranty and support

Warranty handling depends on the component, manufacturer, and retailer. Support questions continue through the order or quote email thread.

Assembly QA

Planned baseline checks before handover

  • BIOS and firmware baseline check
  • Driver and AI tooling installation
  • Thermal and load sanity check
  • Memory and storage health check
  • Local AI smoke test where applicable

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.