Brunobkr/llama.cpp_AlgMor24_github

1## Overview2 3> [!IMPORTANT]4> This example and the RPC backend are currently in a proof-of-concept development stage. As such, the functionality is fragile and5> insecure. **Never run the RPC server on an open network or in a sensitive environment!**6 7The `ggml-rpc-server` allows exposing `ggml` devices on a remote host.8The RPC backend communicates with one or several instances of `ggml-rpc-server` and offloads computations to them.9This can be used for distributed LLM inference with `llama.cpp` in the following way:10 11```mermaid12flowchart TD13    rpcb<-->|TCP|srva14    rpcb<-->|TCP|srvb15    rpcb<-.->|TCP|srvn16    subgraph hostn[Host N]17    srvn[ggml-rpc-server]<-.->dev4["CUDA0"]18    srvn[ggml-rpc-server]<-.->dev5["CPU"]19    end20    subgraph hostb[Host B]21    srvb[ggml-rpc-server]<-->dev3["Metal"]22    end23    subgraph hosta[Host A]24    srva[ggml-rpc-server]<-->dev["CUDA0"]25    srva[ggml-rpc-server]<-->dev2["CUDA1"]26    end27    subgraph host[Main Host]28    local["Local devices"]<-->ggml[llama-cli]29    ggml[llama-cli]<-->rpcb[RPC backend]30    end31    style hostn stroke:#66,stroke-width:2px,stroke-dasharray: 5 532    classDef devcls fill:#5B9BD533    class local,dev,dev2,dev3,dev4,dev5 devcls34```35 36By default, `ggml-rpc-server` exposes all available accelerator devices on the host.37If there are no accelerators, it exposes a single `CPU` device.38 39## Usage40 41### Remote hosts42 43On each remote host, build the backends for each accelerator by adding `-DGGML_RPC=ON` to the build options.44For example, to build the `ggml-rpc-server` with support for CUDA accelerators:45 46```bash47mkdir build-rpc-cuda48cd build-rpc-cuda49cmake .. -DGGML_CUDA=ON -DGGML_RPC=ON50cmake --build . --config Release51```52 53When started, the `ggml-rpc-server` will detect and expose all available `CUDA` devices:54 55```bash56$ bin/ggml-rpc-server57ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no58ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no59ggml_cuda_init: found 1 CUDA devices:60  Device 0: NVIDIA GeForce RTX 5090, compute capability 12.0, VMM: yes61Starting RPC server v3.0.062  endpoint       : 127.0.0.1:5005263  local cache    : n/a64Devices:65  CUDA0: NVIDIA GeForce RTX 5090 (32109 MiB, 31588 MiB free)66```67 68You can control the set of exposed CUDA devices with the `CUDA_VISIBLE_DEVICES` environment variable or the `--device` command line option. The following two commands have the same effect:69```bash70$ CUDA_VISIBLE_DEVICES=0 bin/ggml-rpc-server -p 5005271$ bin/ggml-rpc-server --device CUDA0 -p 5005272```73 74### Main host75 76On the main host build `llama.cpp` with the backends for the local devices and add `-DGGML_RPC=ON` to the build options.77Finally, when running `llama-cli` or `llama-server`, use the `--rpc` option to specify the host and port of each `ggml-rpc-server`:78 79```bash80$ llama-cli -hf ggml-org/gemma-3-1b-it-GGUF -ngl 99 --rpc 192.168.88.10:50052,192.168.88.11:5005281```82 83By default, llama.cpp distributes model weights and the KV cache across all available devices -- both local and remote -- in proportion to each device's available memory.84You can override this behavior with the `--tensor-split` option and set custom proportions when splitting tensor data across devices.85 86### Local cache87 88The RPC server can use a local cache to store large tensors and avoid transferring them over the network.89This can speed up model loading significantly, especially when using large models.90To enable the cache, use the `-c` option:91 92```bash93$ bin/ggml-rpc-server -c94```95 96By default, the cache is stored in the `$HOME/.cache/llama.cpp/rpc` directory and can be controlled via the `LLAMA_CACHE` environment variable.97 98### RDMA transport99 100On Linux systems with RoCEv2-capable NICs (e.g. Mellanox ConnectX), the RPC backend can use RDMA instead of TCP for lower latency and higher throughput. The transport is negotiated automatically -- no changes to command-line usage are required.101 102RDMA is enabled by default when `libibverbs` is found at build time.103 104### Troubleshooting105 106Use the `GGML_RPC_DEBUG` environment variable to enable debug messages from `ggml-rpc-server`:107```bash108$ GGML_RPC_DEBUG=1 bin/ggml-rpc-server109```110 111