mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-03 09:22:01 +00:00
1d36b3670b
* llama : move end-user examples to tools directory --------- Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
86 lines
3.0 KiB
Markdown
86 lines
3.0 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 running `ggml` backend 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]<-.->backend3["Backend (CUDA,Metal,etc.)"]
|
|
end
|
|
subgraph hostb[Host B]
|
|
srvb[rpc-server]<-->backend2["Backend (CUDA,Metal,etc.)"]
|
|
end
|
|
subgraph hosta[Host A]
|
|
srva[rpc-server]<-->backend["Backend (CUDA,Metal,etc.)"]
|
|
end
|
|
subgraph host[Main Host]
|
|
local["Backend (CUDA,Metal,etc.)"]<-->ggml[llama-cli]
|
|
ggml[llama-cli]<-->rpcb[RPC backend]
|
|
end
|
|
style hostn stroke:#66,stroke-width:2px,stroke-dasharray: 5 5
|
|
```
|
|
|
|
Each host can run a different backend, e.g. one with CUDA and another with Metal.
|
|
You can also run multiple `rpc-server` instances on the same host, each with a different backend.
|
|
|
|
## Usage
|
|
|
|
On each host, build the corresponding backend with `cmake` and add `-DGGML_RPC=ON` to the build options.
|
|
For example, to build the CUDA backend with RPC support:
|
|
|
|
```bash
|
|
mkdir build-rpc-cuda
|
|
cd build-rpc-cuda
|
|
cmake .. -DGGML_CUDA=ON -DGGML_RPC=ON
|
|
cmake --build . --config Release
|
|
```
|
|
|
|
Then, start the `rpc-server` with the backend:
|
|
|
|
```bash
|
|
$ bin/rpc-server -p 50052
|
|
create_backend: using CUDA backend
|
|
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
|
|
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
|
|
ggml_cuda_init: found 1 CUDA devices:
|
|
Device 0: NVIDIA T1200 Laptop GPU, compute capability 7.5, VMM: yes
|
|
Starting RPC server on 0.0.0.0:50052
|
|
```
|
|
|
|
When using the CUDA backend, you can specify the device with the `CUDA_VISIBLE_DEVICES` environment variable, e.g.:
|
|
```bash
|
|
$ CUDA_VISIBLE_DEVICES=0 bin/rpc-server -p 50052
|
|
```
|
|
This way you can run multiple `rpc-server` instances on the same host, each with a different CUDA device.
|
|
|
|
|
|
On the main host build `llama.cpp` for the local backend and add `-DGGML_RPC=ON` to the build options.
|
|
Finally, when running `llama-cli`, use the `--rpc` option to specify the host and port of each `rpc-server`:
|
|
|
|
```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
|
|
```
|
|
|
|
This way you can offload model layers to both local and remote 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.
|