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@decagondev
Created May 1, 2026 21:56
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Save decagondev/b3a6a6388d31f07c0b96fde4fbcc1948 to your computer and use it in GitHub Desktop.

Building an AI development workstation incrementally is a strategic move that ensures you have a professional-grade foundation while spreading out the costs. This 4-month plan focuses on upgrading your "brains and memory" first for immediate relief in development, followed by power and storage, and finally the GPU engine.

Build Summary and Budget Overview

Phase Focus Estimated Cost Main Components
Month 1 Foundation & Memory £1,000 - £1,200 CPU, Motherboard, 128GB RAM, Case
Month 2 Power & Storage £350 - £400 1000W ATX 3.1 PSU, Gen5 NVMe SSD
Month 3 Savings/Cooling £100 - £200 Optional: High-end CPU Cooler or fans
Month 4 The GPU "Engine" £1,100 - £2,800 RTX 5080 or RTX 5090

Month 1: The Core Infrastructure (£1,100 - £1,200)

This phase moves you to the modern AM5 platform and resolves your memory bottlenecks immediately.

  • CPU: AMD Ryzen 9 9900X (12-Core) — £338.99
  • Professional 12-core "Zen 5" processor designed for high-performance creative and technical workloads.
  • Motherboard: MSI MAG X870 TOMAHAWK WIFI — £247.99
  • Future-proofed with PCIe 5.0 and USB4 support to ensure no bottlenecks for next-gen GPUs or ultra-fast storage.
  • Memory: 128GB (4x32GB) Corsair Vengeance DDR5-5600 — £1,080.00
  • Massive capacity upgrade from your current 48GB. 128GB is the baseline for professional AI training and large-scale inference.
  • Case: Lian Li Lancool 216 RGB Black — £89.99
  • Optimized for high-performance airflow with two large 160mm front fans and a dedicated PCIe fan bracket for the GPU.

Month 2: Power and High-Speed Storage (£350 - £400)

Prepares your system to handle the power spikes of high-end AI cards and ensures massive model files load in seconds.

  • PSU: Corsair RM1000e (1000W ATX 3.1) — ~£150.00
  • Fully modular unit with native 12V-2x6 support for modern NVIDIA GPUs.
  • Storage: 2TB Crucial T705 Gen5 NVMe SSD — ~£220.00
  • Achieves up to 14,500MB/s speeds, making it ideal for handling large datasets and rapid iteration.

Month 3: Thermal Stabilization (£100 - £150)

Use this month to ensure your powerful CPU stays cool during long inference runs. If you don't already have a high-end AM5 cooler, prioritize this now.

  • Cooler: Arctic Liquid Freezer III 360 — ~£80.00
  • A top-rated AIO that fits perfectly in the top of the Lancool 216.

Month 4: The GPU "Engine" (£1,100 - £2,800)

The final piece of the puzzle that turns your system into an AI powerhouse.

  • GPU Option A: NVIDIA RTX 5080 (16GB) — ~£1,135.00
  • Fastest consumer architecture with the latest CUDA features.
  • GPU Option B: NVIDIA RTX 5090 (32GB) — ~£2,299.00 - £2,800.00
  • The "Holy Grail" for local LLMs, with enough VRAM to run large models at high speeds.

Proactive Recommendation: Before purchasing Month 1, check if your current power supply is ATX 3.1 compliant; if not, you may need a temporary adapter to use your RX 5700 XT with the new motherboard until Month 2. Would you like me to refine any of these components or provide a specific checklist for the Phase 1 assembly?

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