A user is seeking advice on deploying a new server with 4x H100 GPUs (320GB VRAM) for on-premise AI workloads. They are considering a Kubernetes-based deployment with RKE2, Nvidia GPU Operator, and tools like vLLM, llama.cpp, and Litellm. They are also exploring the option of GPU pass-through with a hypervisor. The post details their current infrastructure and asks for potential gotchas or best practices.
A step-by-step guide on building llamafiles from Llama 3.2 GGUFs, including scripting and Dockerization.
- create a custom base image for a Cloud Workstation environment using a Dockerfile
. Uses:
Quantized models from
A deep dive into model quantization with GGUF and llama.cpp and model evaluation with LlamaIndex