62 Blog Posts To Learn About Gpu

1. A GPU, or Graphics Processing Unit, is an electronic circuit specifically designed to rapidly manipulate and alter memory to create images in a frame buffer. While it originated for graphics, it now plays a crucial role in parallel processing tasks like AI, machine learning, and scientific simulations.

2. The article lists 62 free blog posts about GPUs, ranked by reader engagement on HackerNoon. These posts cover various aspects of GPUs, from their use in AI security systems, crypto mining, and data science, to comparisons between specific GPU models for different applications.

3. The post "Hungry GPUs Need Fast Object Storage" discusses MinIO, a fast object storage solution that can handle the high data throughput required by GPUs. Benchmarks show MinIO achieving 325 GiB/s on GETs and 165 GiB/s on PUTs.

4. "5 Best GPUs for Crypto Mining" provides a list of top GPUs for crypto mining in 2021, with options from Nvidia and AMD.

5. "A Deep Dive Into How Many GPUs It Takes to Run ChatGPT" explores the computational requirements to run OpenAI's ChatGPT model, estimating the number of GPUs needed.

6. The comparison between Nvidia's GeForce RTX 4090 gaming graphics card and the powerful server GPU RTX A5000 is presented in "Which GPU is Better for Business, the RTX 4090 or a Server RTX A5000?"

7. "Is GPU Really Necessary for Data Science Work?" questions the necessity of using a GPU for data science tasks, noting the high cost of GPUs and providing price examples in Brazil.

8. "How GPUs are Beginning to Displace Clusters for Big Data & Data Science" discusses the shift from traditional data processing clusters to using consumer-grade GPUs for big data and data science tasks.

9. "VR Ready Graphics Cards: Is Your GPU Ready for Oculus Link and Steam VR?" advises on selecting a VR-ready graphics card, recommending a minimum of a GeForce GTX 970.

10. "Crack Wifi Handshake Using Hashcat in Windows" mentions using GPU power for hacking, though this is not a typical use case.

11. "From 140GB to 4GB: The Art of LLM Quantization" explains how quantization techniques reduce large language model sizes from 140GB to under 4GB, making them compatible with consumer GPUs.

12. "Silicon Valley’s Pied Piper is Now Real Thanks to New Compression Technology" discusses advancements in data compression technology, enabling compression of large AI models like GPTQ to fit on consumer GPUs.

13. "A Guide on How to Use GPU Nodes in Amazon EKS" provides a step-by-step guide to deploying GPU nodes in Amazon Elastic Kubernetes Service (EKS).

14. "Can the Nvidia RTX A4000 ADA Handle Machine Learning Tasks?" asks if the RTX A4000 ADA is suitable for machine learning workloads.

15. "How Do You Choose the Best Server, CPU, and GPU for Your AI?" advises on selecting appropriate processors and GPUs for AI applications to maximize performance.

16. "Top 10 Machine Learning Optimized Graphics Cards" presents a list of graphics cards optimized for machine learning tasks, aiming to help users maximize efficiency while processing large data sets.

17. "The Evolution of Nvidia's Graphics Cards" focuses on Nvidia's dominance in the GPU market, highlighting key cards from the company's lineup.

18. "How to Run a Flask application with free GPU acceleration for students, using PyCharm" describes using free GPU acceleration for deep learning and machine learning projects on a student's computer.

19. "Use plaidML to do Machine Learning on macOS with an AMD GPU" introduces plaidML, a library that enables machine learning on AMD GPUs and integrated Apple GPUs.

20. "How to Build a Training Pipeline on Multiple GPUs" discusses the challenges of fitting large datasets into single CPUs and the need for training pipelines that can utilize multiple GPUs.

21. "Tackling Environmental Issues with Software: How Remote GPU Reduces the Impact of GPUs" argues that remote GPU usage can reduce the environmental impact of data centers by increasing GPU utilization.

22. "Compute is Going to the Currency of the Future," says Sam Altman on the Lex Fridman Podcast, where Altman discusses the potential value of computing power in the future.