How it works
During inference, a framework sends model parameters and input tensors to a device, executes layers as GPU kernels, and returns output tensors or newly generated tokens. Neural networks rely heavily on matrix multiplications and other parallel operations that GPUs can perform across many cores. Libraries such as PyTorch expose CUDA devices, streams, and memory management while higher-level applications hide most of that machinery.
Data placed in VRAM avoids repeated CPU-to-GPU transfers. When everything does not fit, a runtime may split layers across devices or use offloading, trading capacity for transfer overhead.
Image, video, and language-model workloads
Diffusion image generation repeatedly evaluates a denoising network across inference steps; video adds temporal dimensions and often much larger tensors. Language-model generation processes a prompt, then predicts tokens sequentially while reusing a KV cache. These workloads stress hardware differently, so one benchmark cannot summarize every local-AI task.
Where users encounter it
Applications such as ComfyUI and AUTOMATIC1111 normally use a supported GPU when available. KoboldCpp can offload language-model layers to a GPU while retaining other work on the CPU. Hosted services also use accelerators, but customers see queue time, generation time, or credit cost rather than device configuration.
GPU inference versus CPU inference
CPUs have fewer, more general-purpose cores and access system RAM directly; GPUs offer much greater parallel throughput with a separate memory budget. CPU inference can be practical for quantized language models, small models, or machines without compatible accelerators. GPU inference is usually faster for large tensor workloads, but setup, drivers, unsupported operators, and transfer overhead can narrow the advantage.
Limits and measurements
- Tokens per second, images per minute, and latency describe different workloads and should not be compared directly.
- Lower precision may increase throughput and reduce memory but can affect output or require hardware support.
- Batching improves throughput by doing more work together, while potentially increasing per-request latency and memory use.
- Generation speed does not measure factual accuracy, visual quality, or policy behavior.