Collection: AI & Deep Learning Graphics Cards

Local AI workloads have completely changed hardware requirements. Whether you are running parameter-heavy local LLMs via Ollama and LM Studio, generating high-resolution imagery in ComfyUI with Flux.1 and SDXL, or training PyTorch neural networks, consumer AI demands two non-negotiables: raw VRAM capacity and native NVIDIA Tensor Cores. A minimum 16GB framebuffer is essential to avoid crushing CPU offloading bottlenecks on 8B–14B models at standard context windows, while 24GB to 32GB allocations on the GeForce RTX 5090 and RTX 5080 provide the memory bandwidth needed for 32B models, multi-modal workflows, and large batch sizes. This collection features consumer Blackwell powerhouses alongside professional Leadtek RTX Pro workstation cards with ECC memory. Compare specs in our CUDA graphics cards collection or filter by memory size in our 16GB graphics cards hub. In-stock with Australian warranty and fast local dispatch.