Choosing The Right GPU

Which GPU actually fits your workload

GPU choice comes down mainly to VRAM and compute class, and those requirements differ sharply between rendering, inference, and training workloads. VRAM determines how large a model or scene you can load at once — run out of it, and the job fails or falls back to much slower CPU processing regardless of how fast the GPU's cores are. Compute class determines how quickly that workload actually processes once it fits in memory.

For 3D rendering, video encoding, and most inference or API-serving workloads (running an already-trained model to generate responses, not training it), consumer-class GPUs like the RTX 3090, 4090, or 5000 series offer strong price-to-performance — they have enough VRAM for most single-model inference and rendering jobs, at a fraction of the cost of data-center-class cards. Older cards like the P2000 or P4000 suit lighter, less VRAM-intensive tasks or development and testing environments where raw throughput matters less.

Training large models from scratch, fine-tuning large language models, or running multiple large models simultaneously is where data-center GPUs like the A100 and H100 become necessary — they carry far more VRAM (up to 80GB), higher memory bandwidth, and are purpose-built for the kind of sustained, parallel compute that training workloads demand. If a project involves LLMs, large-batch training, or multi-GPU scaling, this tier is generally the realistic starting point rather than an upgrade path from consumer cards.

Every GPU instance is fully dedicated — no sharing, no throttling from other tenants — and comes with root access, so any framework (PyTorch, TensorFlow, CUDA toolkits) can be installed exactly as needed.

Quick Pick By Use Case