Start with the workload rather than the largest-looking GPU. Training usually depends on VRAM, bandwidth, and multi-GPU interconnects; inference depends on model size, concurrency, and latency; rendering depends on VRAM, software compatibility, and sustained runtime; research may need GPU, CPU, RAM, and fast NVMe together.
An anonymous account and USDT-TRC20 settlement can reduce registration and payment friction, but they do not select hardware for you. Include the model, framework, CUDA or ROCm version, data size, ports, and rental term in the request.
Prices are shown in USD and confirmed orders settle in USDT-TRC20. Check the model, quantity, region, and configuration summary before payment, then verify drivers, VRAM, disk, and network after delivery.