Let's learn about Gpu via these 62 free blog posts. They are ordered by HackerNoon reader engagement data. Visit the Learn Repo or LearnRepo.com to find the most read blog posts about any technology.
A GPU (Graphics Processing Unit) is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer. It matters significantly beyond graphics for parallel processing tasks like AI, machine learning, and scientific simulations, driving advancements in computational power.
1. Developing AI Security Systems With Edge Biometrics
Let’s speak about usage of edge AI devices for office entrance security system development with the help of face and voice recognition.
2. Hungry GPUs Need Fast Object Storage
MinIO is capable of the performance needed to feed your hungry GPUs; a recent benchmark achieved 325 GiB/s on GETs and 165 GiB/s on PUTs.
3. 5 Best GPUs for Crypto Mining
Which is the best GPU in 2021? If you are going to buy a powerful graphics card for gaming or cryptocurrency mining, here are our top picks from Nvidia and AMD.
4. A Deep Dive Into How Many GPUs It Takes to Run ChatGPT
Tom Goldstein goes over how many GPUs it will take to run ChatGPT.
5. No-stress CUDA programming using Go and C
Programming CUDA using Go is a bit more complex than in other languages. Although there are some excellent packages, such as mumax, the documentation is poor, lacks examples and it’s difficult to use.
6. Which GPU is Better for Business, the RTX 4090 or a Server RTX A5000?
We shall compare Nvidia's new gaming graphics card, the GeForce RTX 4090, and the powerful RTX A5000 server card.
7. Is GPU Really Necessary for Data Science Work?
A big question for Machine Learning and Deep Learning apps developers is whether or not to use a computer with a GPU, after all, GPUs are still very expensive. To get an idea, see the price of a typical GPU for processing AI in Brazil costs between US $ 1,000.00 and US $ 7,000.00 (or more).
8. How GPUs are Beginning to Displace Clusters for Big Data & Data Science
More recently on my data science journey I have been using a low grade consumer GPU (NVIDIA GeForce 1060) to accomplish things that were previously only realistically capable on a cluster - here is why I think this is the direction data science will go in the next 5 years.
9. VR Ready Graphics Cards: Is Your GPU Ready for Oculus Link and Steam VR?
The best way to find a VR ready graphics card is to simply look at the Oculus Link requirements or PC requirements for individual VR games on Steam. On the low-end, you should at least aim for a GEFORCE GTX 970.
10. Crack Wifi Handshake Using Hashcat in Windows
You can use GPU power for hacking the world.
11. From 140GB to 4GB: The Art of LLM Quantization
Quantization shrinks 140GB LLMs to under 4GB, bringing enterprise AI to consumer GPUs. A deep dive into GPTQ, AWQ, GGUF, and beyond.
12. Silicon Valley’s Pied Piper is Now Real Thanks to New Compression Technology
HBO's Silicon Valley imagined data compression with Pied Piper. Fast forward to 2024, and real-world startups like SQream Blue are making that dream a reality.
13. A Guide on How to Use GPU Nodes in Amazon EKS
In this article, we'll run GPU nodes in AWS EKS in seven simple steps via nvidia-driver and will check basic methods to debug it after the deployment.
14. Can the Nvidia RTX A4000 ADA Handle Machine Learning Tasks?
Is the Nvidia RTX A4000 ADA suitable for Machine Learning?
15. How Do You Choose the Best Server, CPU, and GPU for Your AI?
Artificial intelligence has become critical for various industries. Selecting appropriate processors and graphics cards will enable the best performance.
16. Top 10 Machine Learning Optimized Graphics Cards
How to choose the right graphics card and maximize the efficiency of processing large amounts of data and performing parallel computing.
17. The Evolution of Nvidia's Graphics Cards
This isn't going to cover every single graphics card used in the whole world as there are so many. But we are going to be covering the one company that almost run the whole production and lead the market with a storm. Side note for the Nvidia side the cards I have added are all the main board cards from Nvidia themselves and not custom boards like which MSI and Asus make.
18. How to Run a Flask application with free GPU acceleration for students, using PyCharm
Many Deep learning or Machine Learning projects require GPU acceleration, and getting access to external GPUs or using GPU services by different cloud services can be costly, especially for students.
19. Use plaidML to do Machine Learning on macOS with an AMD GPU
Want to train machine learning models on your Mac’s integrated AMD GPU or an external graphics card? Look no further than PlaidML.
20. How to Build a Training Pipeline on Multiple GPUs
In the current big data regime, it is hard to fit all the data into a single CPU.
21. Tackling Environmental Issues with Software: How Remote GPU Reduces the Impact of GPUs
Demand for accelerated computing brings a large environmental impact. Remote GPU software will sharply reduce that impact via higher utilization of GPUs.
22. "Compute is Going to the Currency of the Future," says Sam Altman on the Lex Fridman Podcast
"I think it will be maybe the most precious commodity in the world," says Sam Altman on the Lex Fridman Podcast. "Compute is going to the currency of the future
23. Asking ChatGPT to Recommend a Graphic Card
ChatGPT is a new AI-driven chatbot that can answer some questions and even write a paragraph of essays.
[24. The ASIC Chronicles:
The Historical Timeline of Bitcoin's Mining Revolution](https://hackernoon.com/the-asic-chronicles-the-historical-timeline-of-bitcoins-mining-revolution)
As soon as Bitcoin was launched, BTC mining was a prosperous endeavorer, but will crypto mining still be 'a thing' after mining rewards halve!?
25. Gonka Launches: A Decentralized Network Redefining AI Compute
By eliminating centralized gatekeepers, Gonka provides builders and researchers with permissionless access.
26. The GPU Bottleneck: Navigating Supply and Demand in AI Development
Discussion with Ahmad about the importance f GPUs for AI development.
27. How to Earn $350 a Month From Your GPU With Sogni and Salad
Sogni and Salad expand decentralized AI infrastructure to allow GPU owners and renters to earn from AI workloads.
28. A Digestible High-Level Overview of CPU & GPU Cores
CPU & GPU - The Basics - A digestible high-level overview of what happens in The Die
29. NVIDIA's 2024 GTC Announcements: GR00t, Blackwell AI, and More
Nvidia announced new AI chips, GR00T, Omniverse, and more at GTC 2024.
30. SpeechPainter: Text-Conditioned Speech Inpainting
We’ve seen image inpainting, which aims to remove an undesirable object from a picture. The machine learning-based techniques do not simply remove the objects, but they also understand the picture and fill the missing parts of the image with what the background should look like. The recent advancements are incredible, just like the results, and this inpainting task can be quite useful for many applications like advertisements or improving your future Instagram post. We also covered an even more challenging task: video inpainting, where the same process is applied to videos to remove objects or people.
31. NVIDIA RTX 6000 Blackwell Server Edition: Tests, Benchmarks & Comparison
NVIDIA has released three versions of the RTX 6000 Blackwell — and it’s precisely the Server Edition that turned out to be the most mysterious. We tested it in
32. Yes, Women Also Belong in STEM
Scientific spaces belong to us just as they belong to everyone else, regardless of gender.
33. How to Prioritize AI Projects Amidst GPU Constraints
A new way to prioritize to maximize value to the business while optimizing for GPU constraints
34. The AI-Energy Nexus: How Energy Availability Will Define AI Competitive Advantage
Energy availability, not compute, will define AI competitive advantage by 2028.
35. NVIDIA is Creating a Problem It Can Profit From
NVIDIA’s RTX 50 series pricing is an absolute joke. The company is not even pretending to care about gamers anymore.
36. How to Deploy RAPIDs on GPU-Enabled Private Cloud
Learn how to set up a GPU-enabled virtual server instance (VSI) on a Virtual Private Cloud (VPC) and deploy RAPIDS using IBM Schematics.
37. The Added Value of GPU-Accelerated Analytics
GPUs are now being put to the test in the three fastest developing applications in today’s tech ecosystem.
38. Batch Processing Role in Deep Learning
With two common buzzwords in AI being Graphics Processing Unit (GPU) and Batch Processing, there is widespread need to run AI efficiently in production.
39. The GPUs for Deep Learning: NVIDIA vs AWS vs Azure and More
Take a deeper dive into what a GPU is, when you should use it or shouldn’t for Deep Learning tasks, and what is the best GPU on-premises and in the cloud in 202
40. How Argentum AI Plans to Make GPU Access as Liquid as Capital Markets
Argentum AI launches human-trained marketplace AI for GPU trading. Could behavioral learning fix compute market inefficiency?
41. 6 Must Buy Graphics Cards for Cryptocurrency Mining
What are the options for the best graphics cards for cryptocurrency mining? Here is a study.
42. SoluAI: Decentralized AI Computing On Blockchain Powered By GPU Networks
The decentralized model not only supports cost-efficiency with a cost reduction of up to 70% for training AI models, but also fosters an inclusive environment,
43. What's The Best GPU for Mining Cryptocurrencies in 2020? AMD vs. Nvidia
The Reds or the Greens? What are the technologies behind? Cool AMD and cooler Nvidia, or vice-versa? Which one makes the most money out of the buck? Answering all these questions in the following article and sharing tips on how to get the best GPU!
44. Nearly 25% of HackerNoon Readers to Opt for AMD GPUs Next
AMD is likely going to be the next GPU of choice for nearly a quarter of HackerNoon readers that participated in a recent poll conducted on our website.
45. AlphaTON Capital Closes $46M AI Infrastructure Expansion to Address Demands for Confidential Compute
Deal to Expand AlphaTON’s Deployment of Telegram’s Cocoon AI Confidential Compute, Achieving 3.82x Projected Equity Multiple
46. QLoRA: Fine-Tuning Your LLMs With a Single GPU
QLoRA is the first paper that showed we can train LLMs on a single GPU. This article explains the approach of QLoRA in simple terms
47. GPUs Trade Complexity for Massive Parallelism: What Every Machine Learning Engineer Should Know
GPUs deliberately simplify per-thread control to pack in far more parallelism
48. Turbocharging AI Sentiment Analysis: How We Hit 50K RPS with GPU Micro-services
The sentiment analysis stack was one big codebase for data ingestion, model inference, logging, and storage. It worked great, until traffic shot up.
49. How AI Is Fighting Monopolies in Sports Advertising With GPUs and Servers
AI and AR technologies allow sports advertising to be customized to different audiences in real time using cloud-based GPU solutions.
50. Why You Should Run Multiple Applications on the Same GPU (and Why it's so Difficult)
While GPUs are being used more and more, many users encounter the problem of not utilizing them properly.
51. When ML Meets Microservices: Engineering for Scalability and Performance
Discover how combining machine learning with microservices architecture enables scalable, high-performance systems by leveraging modular design, efficient data
52. Evaluating vLLM's Design Choices With Ablation Experiments
In this section, we study various aspects of vLLM and evaluate the design choices we make with ablation experiments.
53. Back to the Future with Quantum Computers
Quantum Computers are the closest we have come to time travel.
54. The Shortage of AMD and Nvidia GPUs Causes Gamers Grief
Finding a latest-gen GPU for their launch prices is nearly impossible.
55. Our GPU Was Idle 77% of the Time. Here's How We Fixed It
Pinned memory and non-blocking streams can speed up data transfers.
56. Ethereum’s Node Problem: Who Really Hosts Web3?
Ethereum may be the world’s most decentralized smart contract platform, but look beneath the surface and a different story emerges.
57. GPU Computing for Machine Learning
By taking advantage of the parallel computing capabilities of GPUs, a significant decrease in computational time can be achieved relative to traditional CPU
58. How Cloud GPUs Are Powering the AI Revolution
Discover how Cloud GPUs are transforming the field of AI by providing the computational power needed to make AI smarter, more accessible.
59. Bringing AI to the Datacenter
The majority of the most important enterprise data remains in the corporate data center.
60. Fighting VRAM Overheating: 3 Unexpected Lessons from My First Windows Utility
A developer's log on fixing laptop VRAM overheating during AI workloads. Why Memory Junction hits 105°C and how Pulse Throttling solves it without undervolting.
61. The HackerNoon Newsletter: Changing Keys, Losing Values (2/24/2025)
2/24/2025: Top 5 stories on the HackerNoon homepage!
62. How I Stopped Copy-Pasting My Sanity Away in Product Testing
From endless manual repetition to reusable workflows: Hardware testing finally scales without the grind.
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