Overview

Important

This example and the RPC backend are currently in a proof-of-concept development stage. As such, the functionality is fragile and insecure. Never run the RPC server on an open network or in a sensitive environment!

The rpc-server allows running ggml backend on a remote host. The RPC backend communicates with one or several instances of rpc-server and offloads computations to them. This can be used for distributed LLM inference with llama.cpp in the following way:

flowchart TD
    rpcb<-->|TCP|srva
    rpcb<-->|TCP|srvb
    rpcb<-.->|TCP|srvn
    subgraph hostn[Host N]
    srvn[rpc-server]<-.->backend3["Backend (CUDA,Metal,etc.)"]
    end
    subgraph hostb[Host B]
    srvb[rpc-server]<-->backend2["Backend (CUDA,Metal,etc.)"]
    end
    subgraph hosta[Host A]
    srva[rpc-server]<-->backend["Backend (CUDA,Metal,etc.)"]
    end
    subgraph host[Main Host]
    local["Backend (CUDA,Metal,etc.)"]<-->ggml[llama-cli]
    ggml[llama-cli]<-->rpcb[RPC backend]
    end
    style hostn stroke:#66,stroke-width:2px,stroke-dasharray: 5 5

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Each host can run a different backend, e.g. one with CUDA and another with Metal. You can also run multiple rpc-server instances on the same host, each with a different backend.

Usage

On each host, build the corresponding backend with cmake and add -DGGML_RPC=ON to the build options. For example, to build the CUDA backend with RPC support:

mkdir build-rpc-cuda
cd build-rpc-cuda
cmake .. -DGGML_CUDA=ON -DGGML_RPC=ON
cmake --build . --config Release

Then, start the rpc-server with the backend:

$ bin/rpc-server -p 50052
create_backend: using CUDA backend
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:   no
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA T1200 Laptop GPU, compute capability 7.5, VMM: yes
Starting RPC server on 0.0.0.0:50052

When using the CUDA backend, you can specify the device with the CUDA_VISIBLE_DEVICES environment variable, e.g.:

$ CUDA_VISIBLE_DEVICES=0 bin/rpc-server -p 50052

This way you can run multiple rpc-server instances on the same host, each with a different CUDA device.

On the main host build llama.cpp for the local backend and add -DGGML_RPC=ON to the build options. Finally, when running llama-cli, use the --rpc option to specify the host and port of each rpc-server:

$ bin/llama-cli -m ../models/tinyllama-1b/ggml-model-f16.gguf -p "Hello, my name is" --repeat-penalty 1.0 -n 64 --rpc 192.168.88.10:50052,192.168.88.11:50052 -ngl 99

This way you can offload model layers to both local and remote devices.