[Feature Request] Add RPC Backend / Remote Offload Support #181

Is your feature request related to a problem? Please describe.

Currently, llama-cpp-python exposes several compute backends (CPU, CUDA, Vulkan, SYCL, Metal), but there is no first-class support for llama.cpp's RPC backend. This makes it difficult to:

• Offload tensor computations to a remote GPU machine.
• Share a single GPU across multiple Python processes or containers without each one holding the full model in VRAM.
• Run inference on machines with limited local compute (e.g., a small CPU-only node) while leveraging a powerful remote accelerator.

At the moment, users who need RPC must either build llama.cpp from source with -DGGML_RPC=ON and manage the server binary externally, or resort to fragile workarounds. A native Python API for this would be a huge quality-of-life improvement.

Describe the solution you'd like

It would be wonderful if llama-cpp-python could expose the RPC backend through its existing tensor_split / main_gpu parameters, for example:

from llama_cpp import Llama

llm = Llama(
    model_path="model.gguf",
    n_gpu_layers=-1,
    main_gpu=0,
    rpc_servers=["192.168.1.50:42232"],  # <= new kwarg
    tensor_split=[2, 1]  # <= 0 could be this machine, 1 could be the first remote server
)

Additionally, a small helper to launch the RPC server from Python would be ideal:

from llama_cpp import start_rpc_server

server = start_rpc_server(
    host="0.0.0.0",
    port=42232,
    backend="cuda",   # or "vulkan", "cpu", ...
)
# ... run inference on the client side ...
server.stop()

Even a thin wrapper around ggml-rpc-server started via subprocess would be a great start.

Describe alternatives you've considered

• Manual build + external server: Compile llama.cpp with -DGGML_RPC=ON, run ./ggml-rpc-server & by hand, and point the client at it. Would work, but it's fragile and hard to document for new users.

Additional context

llama.cpp already has a mature RPC backend in C++ that speaks a simple TCP protocol.

Happy to help test if that would be helpful.