Choose GPU VRAM for AI inference and rendering

Estimate memory before ordering

Start with the model format and weight precision, not only the parameter count. For rough planning, an uncompressed 7-billion-parameter model at 16-bit precision needs about 14 GB just for weights, before key/value cache, activations, runtime and driver overhead. Quantisation changes that number, but quality and kernel compatibility must be tested.

Video rendering has different constraints: scene geometry, textures, frame buffers, denoising and application memory behaviour. Estimate peak memory from an actual representative job rather than using a model-inference rule for video workloads.

Benchmark the application that matters

Capture peak VRAM, inference latency, tokens per second, batch size and context window under a realistic workload. For rendering, measure scene load, time per frame and out-of-memory failures. Test a single worker and then the concurrency you expect to use.

Check CUDA or other driver/runtime support against the intended application; a GPU listed on a plan does not by itself guarantee a particular framework version, container setup or library compatibility.

Compare the full monthly cost

Ask whether CPU, system RAM, storage, bandwidth, GPU access mode and operating system are included in the advertised price. Compare a shorter trial period before committing to a long plan. In Nigeria, confirm the final naira amount and renewal terms at checkout.

Record both the acceptance benchmark and a rollback route. If the model spills to system RAM or loads only through aggressive offloading, the machine may technically run it but perform too slowly for the project.

Does Rokocloud guarantee the features described in this guide?

No. This is practical evaluation and setup advice. Available plans, pricing, network controls, OS images, licensing and backups must be confirmed for the selected service before purchasing.