GPU Workstation LLM Setup and I. Going Assistance
Budget: $15 – $25 USD
I need a Windows-based GPU workstation dedicated to running local large-language-model workflows. I need someone who can walk me through the full setup—hardware , CUDA drivers, PyTorch/TensorFlow installs, plus the extra tools I rely on for text-to-video generation and similar AI workloads.
Your first task is to get the machine fully operational: verify BIOS and power settings, install the latest GPU driver stack, configure CUDA/cuDNN, and deploy the core frameworks. From there we’ll layer in local-LLM utilities (e.g., llama.cpp, Ollama) alongside Stable Diffusion or any other video-generation packages I might explore. Clear, repeatable documentation of every step is essential so I can reproduce the environment later.
Once the base system is stable, I’d like ongoing support for version upgrades, troubleshooting, and performance optimisations—ideally via remote sessions scheduled as needed. If you’re comfortable pushing Windows workstations to the limit for generative-AI projects, let’s get started.
Your first task is to get the machine fully operational: verify BIOS and power settings, install the latest GPU driver stack, configure CUDA/cuDNN, and deploy the core frameworks. From there we’ll layer in local-LLM utilities (e.g., llama.cpp, Ollama) alongside Stable Diffusion or any other video-generation packages I might explore. Clear, repeatable documentation of every step is essential so I can reproduce the environment later.
Once the base system is stable, I’d like ongoing support for version upgrades, troubleshooting, and performance optimisations—ideally via remote sessions scheduled as needed. If you’re comfortable pushing Windows workstations to the limit for generative-AI projects, let’s get started.