Choosing a GPU for Machine Learning

Start by defining the workload. Training needs memory for parameters, gradients, optimizer states, activations, and temporary workspaces. Inference needs model weights, input and output buffers, and often a key-value cache for autoregressive models. Peak memory demand depends on model architecture, precision, sequence or image size, batch size, and concurrency. A GPU that cannot fit the working set may require sharding, offloading, smaller batches, or a different model.

Memory capacity and bandwidth are distinct. Capacity determines how much state fits; bandwidth affects how quickly data can move between memory and compute. Some workloads are compute-bound, while others spend substantial time moving weights or activations. Tensor-core or other specialized throughput figures are useful only when the model's operations, precision, and software path can use them.

For multi-GPU work, interconnect affects communication overhead. Data parallel training synchronizes gradients, while model or pipeline parallelism moves activations or parameters. A fast individual device can still scale poorly if links or the software strategy become bottlenecks. For inference, batching and concurrency may improve utilization but raise memory and latency needs.

Compatibility is practical, not optional. Check accelerator architecture, driver and runtime versions, framework support, supported precision, library kernels, container images, and cluster scheduler integration. A model may execute on a device yet lack optimized kernels for key operations. Validate with a representative benchmark rather than relying only on synthetic peak numbers.

Compare total cost and operations: purchase or rental price, power, cooling, availability, memory, performance per watt, and utilization. Measure the actual end-to-end job, including data loading and preprocessing. The right choice may be a smaller or cheaper GPU when the workload is limited by model size, input pipeline, or low request volume.