mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-27 08:21:30 +00:00
c61ae20d05
Update the README file to match the newly added functionality of exposing multiple devices from a single server. Co-authored-by: Diego Devesa <slarengh@gmail.com>
105 lines
3.6 KiB
Markdown
105 lines
3.6 KiB
Markdown
## 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.
|
|
|
|
### 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/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.
|
|
|
|
### Troubleshooting
|
|
|
|
Use the `GGML_RPC_DEBUG` environment variable to enable debug messages from `rpc-server`:
|
|
```bash
|
|
$ GGML_RPC_DEBUG=1 bin/rpc-server
|
|
```
|
|
|