Originally published at: Accelerate Large-Scale LLM Inference and KV Cache Offload with CPU-GPU Memory Sharing | NVIDIA Technical Blog
Large Language Models (LLMs) are at the forefront of AI innovation, but their massive size can complicate inference efficiency. Models such as Llama 3 70B and Llama 4 Scout 109B may require more memory than is included in the GPU, especially when including large context windows. For example, loading Llama 3 70B and Llama 4…
Any ready made Docker container that can leverage this to serve LLMs with an OpenAI compatible API, without one having to re-implement the code shown in the blog post + the web server + metrics, etc…. ? Would the --cpu-offload-gb parameter in vLLM be equivalent, or is some performance left on the table using this instead of RMM ? Thanks