VRAM is often the first hard constraint
GPU model names are useful, but VRAM frequently determines whether the workload can run at all. For AI inference, account for model weights, runtime buffers, KV cache, batch size and concurrency. For rendering, account for geometry, textures and frame buffers. Start by estimating what must fit in GPU memory before comparing raw compute performance.
Inference memory grows with concurrency
A model that fits for one request may need substantially more memory at higher concurrency. Quantization, context length, batching strategy and framework behavior all change the result. When requesting a GPU quote, include the model or model class, expected context or input size and a realistic number of simultaneous requests. These inputs are more useful than simply asking for the fastest GPU.
Rendering memory follows scene complexity
A rendering workload may be constrained by textures, geometry, simulation data or large frame buffers. Provide the renderer, representative scene size and whether multiple jobs need to run concurrently. If a scene exceeds available VRAM, a faster GPU with insufficient memory may still be the wrong choice. Testing a representative workload is preferable to choosing by benchmark alone.
Multiple GPUs do not automatically combine memory
Two GPUs with 24 GB each are not automatically equivalent to one 48 GB GPU. The application must explicitly support multi-GPU execution and decide how models or workloads are partitioned. Some workloads scale well across devices, while others need a single large memory space. State whether your framework already supports the intended multi-GPU topology.
CPU, RAM and storage can bottleneck the GPU
A powerful GPU can remain underutilized if preprocessing, data loading or asset I/O cannot keep it busy. Include CPU requirements, system RAM and local storage throughput in the sizing process. Large model files and datasets may also change how much NVMe capacity is practical. The goal is a balanced server, not the highest-spec GPU attached to an undersized system.
Use monthly dedicated GPU for sustained demand
Dedicated monthly GPU infrastructure is most compelling when utilization is regular enough to justify reserved hardware. Short experiments or sporadic burst jobs may be better suited to hourly cloud services. For sustained inference, rendering queues or continuous video processing, compare the expected monthly utilization and operational control against burst-cloud pricing before deciding.