NVIDIA GeForce GTX 1080 training speed for diffusion models

الإجابة

FP16 TFLOPS and 320 GB/s of memory bandwidth put NVIDIA GeForce GTX 1080 squarely in the class of accelerators targeted at modern transformer workloads. FP32 caps at 8.9 TFLOPS, which still handles most non-AI scientific compute comfortably.

For training from scratch, token throughput roughly tracks FP16 TFLOPS. For production inference on foundation models, throughput tracks bandwidth. Real-world numbers will depend heavily on the framework stack (PyTorch, TensorRT-LLM, vLLM), and can vary 30-50% depending on how aggressively you quantise.

See the NVIDIA GeForce GTX 1080 page for the full spec sheet and comparisons to related GPUs.