Tensor Parallelism for Llama.cpp

A pull request has been submitted to implement tensor parallelism within the Llama.cpp project. This update aims to distribute the inference workload across multiple devices, potentially accelerating response times and increasing overall efficiency.

Tensor parallelism is a technique that divides tensors (the fundamental data structures used in deep learning models) among different processors or GPUs. This allows calculations to be performed in parallel, reducing the time required to complete an inference.

The pull request is available on GitHub, and community feedback is generally positive, highlighting the potential impact on Llama.cpp's performance, especially in scenarios with distributed hardware resources. For those evaluating on-premise deployments, there are architectural trade-offs to consider; AI-RADAR offers analytical frameworks on /llm-onpremise to evaluate these aspects.