Blog de Joe Freeman

NVIDIA Broadcast, on Windows, uses your GPU to improve your PC's audio and video streams for recordings, meetings, or other purposes.  NVIDIA Broadcast sits between your Microphone/Camera and the audio/video capture component of whatever proam you are using.  I described here how NVIDIA Broadcast can use the GPU to make it look like you are always looking at your camera  when recording. NVIDIA Broadcast sometimes can't get access to your microphone or camera when they are already being used  by some other program.  Recently, I had a different problem where NVIDIA Broadcast couldn't get access to my Brio Camera, even though no other program was currently using the problem. I stopped and restarted my NVIDIA and Logitech tools to no avail. It turns out one of the NVIDIA Windows services needed to be restarted.  There was a problem with the instance of the service that was running. Restarting the service it fixed the problem. The best video I found is this...

Brev authentication and 'brev shell' create credentials we can use with other tools, like NVIDIA AI Workbench, to let them SSH into Brev instances. This is useful for those needing something beyond the canned models or provided Jupyter Notebooks YouTube short description of the SSH credentials and host info Revision History Created 2024/11 Fixed NVIDIA capitalization 2025/07

A quick explanation of the pieces of Brev visible to external users and how they fit together.  YouTube: A simplified view of the Brev GPU marketplace that are touched by users Revision History Created 2024/11

NVIDIA NIMs are ready to run pre-packaged containerized models.  The NIMs and their included models are available in a variety of profiles supporting different compute hardware configurations.  You can run the NIMs in an interrogatory mode that will tell you which models are compatible with your GPU hardware. You can then run the NIM with the associated profile.   Sometimes there are still problems and we have to add additional tuning parameters to fit in memory or change data types. In my case, the data type change is because of some bug in the NIM startup detection code.   This article requires additional polish.  It has more than a few rough edges.   NVIDIA NIMs are semi-opaque. You cannot build your own NIM.  NIM construction details are not described by NVIDIA.  Examining NVIDIA Model Container Images The first step is to select models we think can fit and run on our NVIDIA GPU hardware. The first step is to investigate models ...

This is about using a Turing NVIDIA Titan RTX GPU to locally execute code assist LLMs to be used in VSCode. This slightly older card has 24GB of VRAM making it a great local LLM. The Titan RTX is a two-slot dual-fan card. The Titan RTX is currently about the same price as a refurbished Ampere NVIDIA 3090 TI 24GB. There are a bunch of ways to host the code support LLMs. We are using an early release  Ollama   as our LLM service and  continue.dev  VSCode extension as the language service inside VSCode.   This was tested on AMD Ryzen 8 core with 64GB of memory and the Titan RTX.  Related blog articles and videos Several related blogs and videos that cover VSCode and local LLMs Blog  Get AI code assist VSCode with local LLMs using Ollama and the Continue.dev extension - Mac Get AI code assist VSCode with local LLMs using LM Studio and the Continue.dev extension - Windows Rocking an older Titan RTX 24GB as my local AI Code assist...