Son Pham’s Post

We're excited to announce Turing Segment - optimized version of Cellpose leveraging dynamic programming & NVIDIA GPUs to segment your cells in seconds, not hours. Same accuracy, much faster & with less memory usage. Explore & download: https://lnkd.in/gKFGmkqs #SpatialBiology #CellSegmentation #GPU

Hi there, Connections! Having been inspired by Rahul B.'s hashtag #100daysofRTL challenge, I shall attempt to build 100 distinct digital electronics circuits on the Xilinx Vivado Design suit.  I believe that this is a fantastic endeavor to gain a solid understanding of the principles of verilog coding through practice and education. GitHub Repository: https://lnkd.in/dA8qfrcc #100daysofrtl #drgvishwanathanchallenge #verilog #rtl #intel #qualcomm #systemverilog #vhdl #circuitdesign

Last week I posted about NVIDIA’s Chat with RTX and mentioned the installation issues that I faced. This week's post is all about those issues and how to avoid them. Firstly, NVIDIA Chat with RTX is a tech tool and is not yet a commercial-grade quality package. It is not easy to install and run. For example, the system only works on Windows 11 and requires an NVIDIA GeForce™ RTX 30 or 40 Series GPU or NVIDIA RTX™ Ampere or Ada Generation GPU with at least 8GB of VRAM. Additionally, a minimum of 16 GB RAM on your PC is required, and the installation file takes up 35 GB of space just to download. It takes over 90 minutes to download the installation file using gigabit speed. Once downloaded, it took me almost three additional hours to fix all the installation issues. The install kept crashing about 7 minutes into setup and provided no error messages as to why. It turns out the setup will crash if you do not have the absolute latest NVIDIA graphics driver installed. After setup, it starts building the LLM repository and crashes again with the error “You try to use a model that was created with version 2.5.1; however, your version is 2.2.2. This might cause unexpected behavior or errors. In that case, try to update to the latest version.” The program provides feedback on why it crashed but does not tell which program needs to be updated to 2.5.1. After two hours of searching the internet, I found the answer was that the Pooling.py script was pointing to the older versions of the build (see link below.) A quick copy, paste, and restart later and With those critical problems fixed, the LLM should be installed and built without further issues. I now plan to scale up my dataset and report back soon on how it handles thousands of documents. 

🚀 Unlocking Efficient LLM Deployment on CPUs with OpenVINO! 🧠💡 Recently, I took on the challenge of optimizing some heavy-duty LLMs for CPU deployment and the results have been impressive! Using Intel’s OpenVINO toolkit, I compressed the LLaMA-3 (8B) and Phi-2 models, originally 8GB each, down to just 700MB approx, without significant loss in performance. Here’s a glimpse into how you can achieve similar results and run these models efficiently on a CPU using Gradle for build automation. 🔧 Model Compression with OpenVINO 1. Convert the model to OpenVINO Intermediate Representation (IR) and compress model weights to INT4 using NNCF. 2. Apply stateful transformation for automatic handling model state. 3. Select a device for inference (CPU, GPU or NPU). 4. Estimate model performance. 5. Compare with model without state. 6. Using a model with Optimum Intel. 7. Interactive chatbot demo. 🌐 Why This Matters Resource Efficiency: Deploy powerful LLMs on CPUs, ideal for edge devices and environments where GPU resources are limited. Cost-Effective: Leverage existing CPU infrastructure to save on computational costs. Scalable: Facilitate broad deployment of LLMs without extensive hardware requirements. OpenVINO’s ability to compress and optimize LLMs bridges the gap between computational demands and resource constraints, making it easier to deploy advanced AI solutions broadly. 🌍 useful links: https://lnkd.in/gpTREdph https://lnkd.in/g5Un22N9 Enjoy coding LLM on your own CPU. Could you try and let me know how it worked for your application? #llm #ai #MachineLearning #OpenVINO #LLM #ModelCompression #EdgeComputing #CPUEfficiency #Inference #Quantization #Pruning #AIOptimization #DeepLearning #TechInnovation #Gradle #Intel #NaturalLanguageProcessing #ModelOptimization #AIModels #AIDeployment #AIonCPUs #intel #datascience #datascientist #ai

Performing CPU brush drawing on Texture2D poses performance issues. The common solution is to use a GPU brush. However, there is currently limited adoption of the practice of drawing on Texture2DArray. The video demonstrates this aspect. https://lnkd.in/gMq6Wb4V

Chrome gives all *.google.com sites full access to system / tab CPU usage, GPU usage, and memory usage. It also gives access to detailed processor information, and provides a logging backchannel. This API is not exposed to other sites - only to *.google.com

Have you ever wanted to get into AI text to image generation but don't want to pay anything for it? If you have a decent graphics card, you can always run this on your own machine. You could run it without a good GPU, but I've heard it's slow. Fooocus is a great way to get started in the text to image generation field. There are many alternatives, but I've found this one to be a great starting spot! #ai #stablediffusion #gpu #image https://lnkd.in/da_mXkXe

Matt Oztalay's talks on optimizing UE5 are amazingly in-depth and you should NOT skip on these, there's knowledge here that hasn't been shared anywhere else! We just uploaded Part 1 on Nanite and Lumen, right here: https://lnkd.in/dYHf8DYx Part 2 focuses on VSMs, TSR, and World Partition performance, another treasure trove of advanced knowledge. Click it! https://lnkd.in/dnqhiBqU

I'm excited to announce 🎉 the release of FoliCon v3.11.0! 🎉 In this new update, I've added and fixed some fantastic features: ✨ What's New? 1. Dark mode support 🕶️ - I've added dark mode support to make the application easier on the eyes during late-night sessions. Check out more about the addition: [PR #137](https://lnkd.in/dbWwjhwX) 🐛 What's Fixed? 1. Auto mode video/trailer visibility 🎥 - Resolved the issue where videos/trailers were not showing in Auto mode. 2. Drag and drop issue in Custom Icon setter 👆 - The capability to drag and drop between Icons and folders list-box in Custom Icon setter has now been fixed. 📢 Important Note: From this release, the self-contained release has been discontinued (open for discussion). FoliCon still requires .NET 6.0, which you can download [here](https://lnkd.in/ddva8AYa). Your feedback on this new release will be highly appreciated! Please feel free to share your comments and suggestions. Thank you for your continued support! https://lnkd.in/dtrUVz23

Discover how hardware-accelerated GPU scheduling affects your content creation workflow! Our latest review reveals minimal impact on most applications, with a notable 10% performance boost in After Effects. Dive into the details to understand how this feature can optimize your creative process. https://hubs.ly/Q02bG8tc0