## 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 exposing `ggml` devices 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]<-.->dev4["CUDA0"]
    srvn[rpc-server]<-.->dev5["CPU"]
    end
    subgraph hostb[Host B]
    srvb[rpc-server]<-->dev3["Metal"]
    end
    subgraph hosta[Host A]
    srva[rpc-server]<-->dev["CUDA0"]
    srva[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, `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 `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 `rpc-server` will detect and expose all available `CUDA` devices:

```bash
$ bin/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/rpc-server -p 50052
$ bin/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 `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.

```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
```

By default, the cache is stored in the `$HOME/.cache/llama.cpp/rpc` directory and can be controlled via the `LLAMA_CACHE` environment variable.

### Troubleshooting

Use the `GGML_RPC_DEBUG` environment variable to enable debug messages from `rpc-server`:
```bash
$ GGML_RPC_DEBUG=1 bin/rpc-server
```