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rpc : update README [no ci] (#9320)
Update README with instructions how to offload model layers to both local and remote devices
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@ -10,20 +10,21 @@ This can be used for distributed LLM inference with `llama.cpp` in the following
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```mermaid
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flowchart TD
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rpcb---|TCP|srva
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rpcb---|TCP|srvb
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rpcb-.-|TCP|srvn
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rpcb<-->|TCP|srva
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rpcb<-->|TCP|srvb
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rpcb<-.->|TCP|srvn
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subgraph hostn[Host N]
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srvn[rpc-server]-.-backend3["Backend (CUDA,Metal,etc.)"]
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srvn[rpc-server]<-.->backend3["Backend (CUDA,Metal,etc.)"]
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end
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subgraph hostb[Host B]
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srvb[rpc-server]---backend2["Backend (CUDA,Metal,etc.)"]
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srvb[rpc-server]<-->backend2["Backend (CUDA,Metal,etc.)"]
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end
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subgraph hosta[Host A]
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srva[rpc-server]---backend["Backend (CUDA,Metal,etc.)"]
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srva[rpc-server]<-->backend["Backend (CUDA,Metal,etc.)"]
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end
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subgraph host[Main Host]
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ggml[llama.cpp]---rpcb[RPC backend]
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local["Backend (CUDA,Metal,etc.)"]<-->ggml[llama-cli]
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ggml[llama-cli]<-->rpcb[RPC backend]
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end
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style hostn stroke:#66,stroke-width:2px,stroke-dasharray: 5 5
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```
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@ -62,17 +63,11 @@ $ CUDA_VISIBLE_DEVICES=0 bin/rpc-server -p 50052
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This way you can run multiple `rpc-server` instances on the same host, each with a different CUDA device.
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On the main host build `llama.cpp` only with `-DGGML_RPC=ON`:
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```bash
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mkdir build-rpc
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cd build-rpc
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cmake .. -DGGML_RPC=ON
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cmake --build . --config Release
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```
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Finally, use the `--rpc` option to specify the host and port of each `rpc-server`:
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On the main host build `llama.cpp` for the local backend and add `-DGGML_RPC=ON` to the build options.
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Finally, when running `llama-cli`, use the `--rpc` option to specify the host and port of each `rpc-server`:
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```bash
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$ 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
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```
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This way you can offload model layers to both local and remote devices.
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