| ## 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 `ggml-rpc-server` allows exposing `ggml` devices on a remote host. |
| The RPC backend communicates with one or several instances of `ggml-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[ggml-rpc-server]<-.->dev4["CUDA0"] |
| srvn[ggml-rpc-server]<-.->dev5["CPU"] |
| end |
| subgraph hostb[Host B] |
| srvb[ggml-rpc-server]<-->dev3["Metal"] |
| end |
| subgraph hosta[Host A] |
| srva[ggml-rpc-server]<-->dev["CUDA0"] |
| srva[ggml-rpc-server]<-->dev2["CUDA1"] |
| end |
| subgraph host[Main Host] |
| local["Local devices"]<-->ggml[llama-cli] |
| ggml[llama-cli]<-->rpcb[RPC backend] |
| end |
| style hostn stroke:#66,stroke-width:2px,stroke-dasharray: 5 5 |
| classDef devcls fill:#5B9BD5 |
| class local,dev,dev2,dev3,dev4,dev5 devcls |
| ``` |
|
|
| By default, `ggml-rpc-server` exposes all available accelerator devices on the host. |
| If there are no accelerators, it exposes a single `CPU` device. |
|
|
| ## Usage |
|
|
| ### Remote hosts |
|
|
| On each remote host, build the backends for each accelerator by adding `-DGGML_RPC=ON` to the build options. |
| For example, to build the `ggml-rpc-server` with support for CUDA accelerators: |
|
|
| ```bash |
| mkdir build-rpc-cuda |
| cd build-rpc-cuda |
| cmake .. -DGGML_CUDA=ON -DGGML_RPC=ON |
| cmake --build . --config Release |
| ``` |
|
|
| When started, the `ggml-rpc-server` will detect and expose all available `CUDA` devices: |
|
|
| ```bash |
| $ bin/ggml-rpc-server |
| ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no |
| ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no |
| ggml_cuda_init: found 1 CUDA devices: |
| Device 0: NVIDIA GeForce RTX 5090, compute capability 12.0, VMM: yes |
| Starting RPC server v3.0.0 |
| endpoint : 127.0.0.1:50052 |
| local cache : n/a |
| Devices: |
| CUDA0: NVIDIA GeForce RTX 5090 (32109 MiB, 31588 MiB free) |
| ``` |
|
|
| You 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: |
| ```bash |
| $ CUDA_VISIBLE_DEVICES=0 bin/ggml-rpc-server -p 50052 |
| $ bin/ggml-rpc-server --device CUDA0 -p 50052 |
| ``` |
|
|
| ### Main host |
|
|
| On the main host build `llama.cpp` with the backends for the local devices and add `-DGGML_RPC=ON` to the build options. |
| Finally, when running `llama-cli` or `llama-server`, use the `--rpc` option to specify the host and port of each `ggml-rpc-server`: |
|
|
| ```bash |
| $ llama-cli -hf ggml-org/gemma-3-1b-it-GGUF -ngl 99 --rpc 192.168.88.10:50052,192.168.88.11:50052 |
| ``` |
|
|
| By 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. |
| You can override this behavior with the `--tensor-split` option and set custom proportions when splitting tensor data across 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/ggml-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. |
|
|
| ### RDMA transport |
|
|
| The RPC backend can use RDMA instead of TCP for lower latency and higher throughput. The transport is negotiated during the initial handshake -- no changes to command-line usage are required, and the connection falls back to TCP unless both peers can use RDMA. |
|
|
| Two providers are supported, each enabled by default when its library is found at build time: |
|
|
| - **Linux**: RoCEv2-capable NICs (e.g. Mellanox ConnectX), via `libibverbs`. |
| - **macOS**: RDMA over Thunderbolt on Apple silicon Macs with Thunderbolt 5, via `librdma`. Requires macOS 26.2 or later, with RDMA enabled once from macOS Recovery via `rdma_ctl enable`. See [TN3205](https://developer.apple.com/documentation/technotes/tn3205-low-latency-communication-with-rdma-over-thunderbolt). |
|
|
| RDMA is point-to-point, so each side uses the local device whose GID matches the address the connection was made on. Connect over the RDMA-capable link -- with Thunderbolt, use the peer's Thunderbolt address in `--rpc`; a connection made over another interface stays on TCP. |
|
|
| To force plain TCP without rebuilding, set `GGML_RPC_NO_RDMA` on either peer: |
| ```bash |
| $ GGML_RPC_NO_RDMA=1 bin/ggml-rpc-server |
| ``` |
|
|
| ### Troubleshooting |
|
|
| Use the `GGML_RPC_DEBUG` environment variable to enable debug messages from `ggml-rpc-server`: |
| ```bash |
| $ GGML_RPC_DEBUG=1 bin/ggml-rpc-server |
| ``` |
|
|
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|
