Buckets:
| ## 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 | |
| The `GGML_RPC_DEBUG` environment variable controls the verbosity of the logs emitted by the RPC backend. | |
| It can be set on the server, on the client (e.g. `llama-cli`), or both. Larger values produce more detailed output: | |
| - unset / `0` - disabled (only warnings and errors are printed) | |
| - `1` - high-level events: connections, handshake, buffer allocations, graph computes, cache evictions | |
| - `2` - per-command trace: every RPC message sent/received, tensor transfers, cache hits/misses | |
| - `3` - fine detail: per-command timings, graph node dumps, transport byte counts | |
| Non-numeric values are treated as `1`. | |
| ```bash | |
| $ GGML_RPC_DEBUG=1 bin/ggml-rpc-server | |
| ``` | |
| > [!NOTE] | |
| > The debug levels are emitted as `ggml` debug records, which `llama.cpp` applications drop unless the | |
| > global verbosity threshold is raised. On the client, pass `-lv 5` together with `GGML_RPC_DEBUG`: | |
| ```bash | |
| $ GGML_RPC_DEBUG=2 bin/llama-cli -hf ggml-org/gemma-3-1b-it-GGUF -ngl 99 --rpc 192.168.88.10:50052 -lv 5 | |
| ``` | |
Xet Storage Details
- Size:
- 5.79 kB
- Xet hash:
- a1091fc8cc441e68f9a224858f835465fdd81236171107f7c82884cf5c1cb30d
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.