| ## 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: | |
| ```mermaid | |
| 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 | |
| ``` | |
| 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: | |
| ```bash | |
| 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: | |
| ```bash | |
| $ 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.: | |
| ```bash | |
| $ 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`: | |
| ```bash | |
| $ 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. | |
| ### Local cache | |
| The RPC server can use a local cache to store large tensors and avoid transferring them over the network. | |
| This can speed up model loading significantly, especially when using large models. | |
| To enable the cache, use the `-c` option: | |
| ```bash | |
| $ bin/rpc-server -c | |
| ``` | |
| By default, the cache is stored in the `$HOME/.cache/llama.cpp/rpc` directory and can be controlled via the `LLAMA_CACHE` environment variable. | |