Why VRAM is the number that matters most
Model size determines how much VRAM you need before anything else — CPU and RAM matter, but a GPU that runs out of VRAM simply can't load the model at all, regardless of how fast the rest of the PC is. As a rough local rule of thumb: 8–12GB is workable for small models and image generation, 16–24GB opens up mid-size LLM fine-tuning and Stable Diffusion at higher resolutions, and 24GB+ is where serious local training and larger models become realistic.
GPU options for AI, priced in Uganda
| GPU | VRAM | Good for | Price (UGX, card only) |
|---|---|---|---|
| RTX 5080 | 16GB GDDR7 | Fine-tuning smaller models, Stable Diffusion | 8,000,000 |
| RTX 4090 | 24GB GDDR6X | Serious local training, larger LLMs, multi-task inference | 15,500,000 |
| RTX 5090 | 32GB GDDR7 | Largest local models, fastest training iteration | 26,500,000 |
Complete AI workstation builds we stock
If you'd rather buy a complete, tested system instead of assembling parts yourself:
- Creator RTX 4090 Legacy — Ryzen 9 9900X, RTX 4090 24GB, 64GB DDR5 — UGX 25,200,000
- Flagship (RTX 5090) — Core i9 14th Gen, RTX 5090 32GB, 32GB DDR5 — UGX 32,400,000
- Threadripper Workstation — Ryzen Threadripper 7980X, RTX 4090 24GB, 128GB DDR5 ECC — UGX 58,500,000 (for serious multi-model training and workstation-grade reliability)
RAM and storage — don't skip these
For AI work, pair your GPU with at least 32GB of system RAM (64GB+ if you're also doing data preprocessing locally), and use NVMe storage — dataset loading speed becomes a real bottleneck on a SATA SSD once you're iterating on large training runs.
Tell us what you're training or running locally (LLM fine-tuning, Stable Diffusion, model inference) and your VRAM target — we'll quote a build.
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