Deploying LLM on RunPod
InnovationM
APRIL 25, 2024
Engineered to harness the power of GPU and CPU resources within Pods, it offers a seamless blend of efficiency and flexibility through serverless computing options. This typically involves saving the model weights and architecture in a compatible format, such as a TensorFlow SavedModel or PyTorch state dictionary.
Let's personalize your content