A team building a defect-detection model for a production line starts small: a few hundred labelled photos, a laptop GPU, training finishes in twenty minutes. Then the dataset grows to fifteen thousand images — because that's what the model actually needs to be reliable, not because anyone got carried away — and the same laptop that handled the toy version is still training when the team gets in the next morning.
Computer vision training is bound by three things, roughly in this order: GPU VRAM, which caps how many images you can process in a single batch and therefore how fast and how stable training is; a CPU with enough real throughput to keep the data-augmentation pipeline feeding the GPU instead of starving it; and storage fast enough to read a large image dataset repeatedly without becoming the bottleneck itself. At Sephora's August 2026 prices, an RTX 5070 Ti (16GB) at ₦1,700,000 handles most production computer-vision work comfortably. Step up to the RTX 5090 (32GB) at ₦7,600,000 if you're working with high-resolution imagery — medical, satellite, industrial-inspection scale — or training larger architectures where shrinking batch size to fit a smaller card isn't an option.
Why VRAM Is the Real Ceiling
Every image in a training batch, plus the model's weights and the intermediate activations needed to compute gradients, has to fit in GPU memory at once. Run out of VRAM and the only options are to shrink the batch size (slower, and can hurt training stability — very small batches produce noisier gradient estimates) or resize your images down (which can quietly cost you the fine detail your model actually needed to see). Neither is a fix; both are compromises made because the card ran out of room.
16GB is a genuinely workable floor for most production computer-vision training — object detection, classification, and segmentation on typical dataset sizes fit comfortably. Where 16GB starts to feel tight is high-resolution work: a 4K industrial-inspection frame or a full-size satellite tile eats far more VRAM per image than a standard 224×224 classification crop, and you'll find yourself capping batch size lower than you'd like well before you hit any compute limit. That's the case for the RTX 5090's 32GB, not a hypothetical one.
| GPU | VRAM | Sephora price (Aug 2026) | Good for |
|---|---|---|---|
| RTX 5070 Ti | 16GB | ₦1,700,000 | Most production CV training — detection, classification, segmentation at typical resolutions |
| RTX 5080 | 16GB | ₦2,600,000 | Same VRAM ceiling as the 5070 Ti, faster core — worth it when training speed itself is the constraint |
| RTX 5090 | 32GB | ₦7,600,000 | High-resolution imagery, larger architectures, bigger batches without compromise |
Worth naming directly: the RTX 4090 (24GB) sits between the 5080 and 5090 on paper, but Nigeria's retail market has no clean published price for it right now — the one Nigerian seller we found stocking it asks buyers to contact them directly rather than publish a figure, a real but opaque-pricing situation, not a gap in how carefully we checked. Unless you have a specific, named reason to need its exact 24GB profile, the 5080 or 5090 gets you a card Sephora can quote precisely today.
The CPU Side of Training: Data Augmentation Is a Real Job
It's easy to assume the GPU does all the work in a training run, and for the matrix math, it does. But before an image reaches the GPU, something has to decode it, resize it, and apply the random crops, flips, and colour-jitter transforms that make a model generalise instead of memorising — and that work runs on the CPU, once per image, every single epoch. If your CPU can't keep that pipeline moving fast enough, the GPU finishes its batch and sits waiting for the next one, which means you've paid for GPU throughput you aren't actually using.
For most computer-vision workloads, a current-generation chip in the Core Ultra 9 285K class (₦1,256,899 at Sephora's August 2026 catalogue pricing) has enough real cores to keep a typical augmentation pipeline fed. Worth flagging the same finding our other AI-hardware pricing covers: Nigeria's general retail market barely carries standalone desktop CPUs at all — a custom-build shop pricing the chip as a component, the way Sephora does, is the honest way to get a real number for one, not a shelf price you'll find on a general marketplace. If your specific pipeline runs unusually heavy augmentation — large images, many transform steps, several workers in parallel — a higher-core-count Ryzen 9 (up to the 9950X3D at ₦1,595,781) earns its price; for a typical pipeline, it's money better spent moving up a GPU tier instead.
Storage: Datasets Get Read Constantly, Not Once
A training run doesn't read your dataset once — it reads through the entire thing, in full, every single epoch, often for dozens or hundreds of epochs before a model converges. A slow drive turns that into a bottleneck no GPU upgrade fixes. Sephora's catalogue tops out at a 2TB PCIe 4.0 NVMe drive (₦409,995) or a 2TB PCIe 5.0 NVMe drive (₦615,002) for maximum read throughput; either is a sensible working floor for a serious CV project once you account for the raw dataset, augmented copies generated during preprocessing, and multiple model checkpoints saved during training — all of which add up faster than the dataset's raw download size suggests. For archived datasets you're not actively training against, a bulk HDD (up to 24TB at ₦1,429,995) is the honest place to put them rather than crowding your fast working drive.
Worth being direct about a boundary here: Sephora's catalogue is consumer-grade NVMe and SATA storage, not true enterprise-tier (U.2, high-endurance, dual-port) drives. For most computer-vision teams that's not a real limitation — the workload is read-heavy sequential access, exactly what consumer NVMe is good at — but if your project truly needs enterprise storage guarantees, that's a conversation to have directly rather than something the standard catalogue can quote.
Common Computer Vision Training Mistakes
The most common mistake isn't a hardware choice at all — it's sizing the machine for the demo dataset instead of the real one. A model that trains fine on two thousand images can behave very differently at twenty thousand, both in how long training takes and in how much VRAM headroom a larger, more varied batch actually needs. Size the build for where the project is going, not where it happens to be during the first proof of concept.
The second is treating augmentation as free. Aggressive augmentation — heavy rotation, colour jitter, multiple crops per source image — really does improve model generalisation, but every one of those transforms is CPU work happening on every epoch. A pipeline that looks fine at a small batch size can reveal a CPU bottleneck once you scale batch size up to actually use a bigger GPU's VRAM, which is a frustrating way to discover that the GPU upgrade didn't deliver the speed-up you expected.
The third is skipping framework and driver compatibility checks until something breaks. Computer vision training stacks (PyTorch, CUDA, cuDNN, and whatever vision-specific libraries a project depends on) have real version interdependencies, and a mismatch here wastes far more time than it should for a problem that's entirely avoidable. Our CUDA compatibility guide covers how to get this right before it costs you a debugging afternoon that has nothing to do with your actual model.
Nigeria Context: Power and Runtime, Not Just Price
A dedicated CV training box tends to run flat-out for hours at a stretch, unlike a workstation doing occasional inference — and Nigeria's grid reality makes that matter in a way it might not elsewhere. Plan your power setup around sustained draw, not peak draw, and don't assume a UPS sized for a typical office PC will carry a GPU running at full load for an eight-hour training job. If that's new territory, our workstation UPS sizing guide walks through the actual VA math for a sustained AI-class load rather than a rule of thumb borrowed from gaming PCs.
When One GPU Isn't Enough
Most computer-vision projects never need more than one GPU — the jump to multi-GPU is a real complexity cost, not a free speed-up, and it's worth avoiding until you have a concrete reason to need it. If your models or dataset grow to the point where a single RTX 5090 genuinely can't get you an acceptable training time, our multi-GPU training guide covers what that jump actually costs and when it pays for itself. And once a model is trained and you're deploying it for inference rather than training it further, our quantisation guide covers how to shrink it to run faster and cheaper without retraining from scratch.
Getting the Rest of the Build Right
GPU, CPU, and storage are the decisions that actually change what you can train — the rest of the build (motherboard, case, PSU sizing) matters but doesn't move the needle the same way. If you want the full breakdown of how CPU and RAM choices interact with a GPU-heavy build like this one, our CPU/RAM/storage deep-dive covers the parts people get wrong more often than the GPU itself. And if your work sits closer to running and querying models than training them from scratch, our RAG and vector-database workstation guide covers a related but meaningfully different hardware profile.
For most computer-vision teams, the honest recommendation is the RTX 5070 Ti tier: enough VRAM to train comfortably without constant compromise, paired with a current-generation CPU that won't starve the pipeline and real NVMe headroom for the dataset. Sephora Systems, a custom PC and workstation builder based in Abuja with an experience centre in Gwarimpa, can configure a build around this exact profile — start from a computer-vision-tuned build in the configurator and adjust from there rather than assembling the spec from scratch.