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Damian Bogunowicz: Why GPU Clusters Don't Need to Go Brrr?
⏲ 25 min 66 sec ✓ 19-Apr-2023
Mythbusters Demo GPU versus CPU
⏲ 1 minute 34 seconds 👁 2.8M
SPEED UP your RENDERS in DaVinci RESOLVE - GPU Rendering
⏲ 3 minutes 4 seconds 👁 37.2K
Python Programmers have GPUs too - Jeremy Singer
⏲ 17 min 60 sec ✓ 07-Jul-2020
Safely Overclock Your NVIDIA GPU (One-Click Method, Official)
⏲ 46 seconds 👁 36.8K
Learn to use a CUDA GPU to dramatically speed up code in Python.
⏲ 9 minutes 5 seconds 👁 3.5K
Machine Learning speedup using GPU compared to CPU.kNN Classifier Example.1 hour vs 14 hours.
⏲ 35 minutes 31 seconds 👁 30
100x Faster Than NumPy... (GPU Acceleration)
⏲ 28 minutes 49 seconds 👁 40.5K
How to Optimize your NVIDIA GPU - Improve FPS and Performance!
⏲ 10 minutes 11 seconds 👁 38.2K
Are you maximizing your RAM speed?
⏲ 45 seconds 👁 794K
One Trick To Speed Up Your PC - GPU Hardware Scheduling #shorts
⏲ 34 seconds 👁 64.5K
Learn to Use a CUDA GPU to Dramatically Speed Up Code In Python
⏲ 9 minutes 6 seconds 👁 39.5K
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_damian-bogunowicz-why-gpu-clusters-don39t-need-to-go-brrr.webp "Forget specialized hardware. Get GPU-class performance on your commodity CPUs with compound sparsity and sparsity-aware inference execution.nThis talk will demonstrate the power of compound sparsity for model compression and inference speedup for NLP and CV domains, with a special focus on the recently popular Large Language Models. The combination of structured + unstructured pruning (to 90%+ sparsity), quantization, and knowledge distillation can be used to create models that run an order of m")
_mythbusters-demo-gpu-versus-cpu.webp "NVIDIA")
_speed-up-your-renders-in-davinci-resolve-gpu-rendering.webp "George Demner")
_python-programmers-have-gpus-too-jeremy-singer.webp "Auto-parallelization fits the Python philosophy, provides effective performance, and is convenient for non-expert developers. Despite being a dynamic language, we show that Python is a suitable target for auto-parallelization. We show that staging dependence analysis is an effective way to maximize performance. We apply classical dependence analysis techniques, then leverage the Python runtime’s rich introspection capabilities to resolve additional loop bounds and variable types in a just-in-t")
_safely-overclock-your-nvidia-gpu-one-click-method-official.webp "KUPOkinz")
_learn-to-use-a-cuda-gpu-to-dramatically-speed-up-code-in-python.webp "Pragmatic AI Labs")
_machine-learning-speedup-using-gpu-compared-to-cpu-knn-classifier-example-1-hour-vs-14-hours.webp "Aethyn")
_100x-faster-than-numpy-gpu-acceleration.webp "Mr. P Solver")
_how-to-optimize-your-nvidia-gpu-improve-fps-and-performance.webp "BareFox")
_are-you-maximizing-your-ram-speed.webp "Linus Tech Tips")
_one-trick-to-speed-up-your-pc-gpu-hardware-scheduling-shorts.webp "UFD Tech")
_learn-to-use-a-cuda-gpu-to-dramatically-speed-up-code-in-python.webp "Pragmatic AI Labs")
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