Blog de Joe Freeman

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 of the different types by visiting the appropriate NVI

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 on Windows 11, Ollama and VS Code