PyTorch model running 170x slower on T4 vs A100. What could cause a bottleneck t

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1t4uj9h/mtp_on_strix_halo_with_llamacpp_pr_22673/"> <img alt="MTP on strix halo with llama.cpp (PR #22673)"

<p>Last month I was helping a friend debug a training loop that was running at maybe 15% GPU utilization on an A100. Fifteen percent. On a card that costs more than my first car.

<!-- SC_OFF --><div class="md"><p>Last year researchers affiliated with NVIDIA, University of Warsaw, and University of Edinburgh published <a

<!-- SC_OFF --><div class="md"><p>I'm reproducing a published paper's hybrid Gabor + CNN architecture in PyTorch. The original implementation is in TensorFlow. My reproduction

<!-- SC_OFF --><div class="md"><p>Every output token the model generates requires reading the entire KV cache from High Bandwidth Memory.</p> <p>Here's what that actually looks

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1t71y36/what_should_a_pytorch_training_endofrun/"> <img alt="What should a PyTorch training end-of-run

<!-- SC_OFF --><div class="md"><p>Hey, </p> <p>Tldr: use local models like qwen 3.5 quantized with proprietary models for fire and forget work. Local model doing the grunt work.

<!-- SC_OFF --><div class="md"><p><a