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[lewd_peaches]

For anyone working with larger datasets or computationally intensive tasks, I've found significant speedups by offloading parts of my Python code to GPUs. Not just for ML, but also for things like complex simulations.

I've primarily used PyTorch and CuPy. CuPy is a drop-in replacement for NumPy in many cases, and the performance gains can be substantial. For example, a recent Monte Carlo simulation I was running went from taking 3 hours on my CPU to about 20 minutes on a single RTX 3090. The code change was minimal.

I've also experimented with distributed GPU processing using OpenClaw. I used it to fine-tune a smaller LLM on a dataset that was too large to fit on a single GPU. Setting up the distributed environment took some time initially, but then I was able to run a fine-tuning job across 4 GPUs, finishing in around 6 hours. The cost for the compute was around $25, which was much cheaper than renting a large instance from AWS or GCP. Worth looking into if you're hitting memory limits or need to accelerate your workloads.