Run LLMs on weak devices or make powerful devices even more powerful by distributing the workload and dividing the RAM usage. This project proves that it's possible split the workload of LLMs across multiple devices and achieve a significant speedup. Distributed Llama allows you to run huge LLMs in-house. The project uses TCP sockets to synchronize the state. You can easily configure your AI cluster by using a home router.
Distributed Llama running Llama 2 70B on 8 Raspberry Pi 4B devices
Supported models:
- Llama 2 (7B, 13B, 70B) chat and non-chat versions,
- Llama 3,
- Grok-1 (314B).
Known limitations:
- You can run Distributed Llama only on 1, 2, 4... 2^n devices.
- Optimized for (weights format × buffer format):
- ARM CPUs
- ✅ F32 × F32
- ❌ F16 × F32
- ❌ Q40 × F32
- ✅ Q40 × Q80
- x86_64 AVX2 CPUs
- ❌ F32 × F32
- ❌ F16 × F32
- ❌ Q40 × F32
⚠️ Q40 × Q80 (partial optimization)
- ARM CPUs
Architecture
The project is split up into two parts:
- Root node - it's responsible for loading the model and weights and forward them to workers. Also, it synchronizes the state of the neural network. The root node is also a worker, it processes own slice of the neural network.
- Worker node - it processes own slice of the neural network. It doesn't require any configuration related to the model.
You always need the root node and you can add 2^n - 1 worker nodes to speed up the inference. The RAM usage of the neural network is split up across all nodes. The root node requires a bit more RAM than worker nodes.
All tests below utilized Q40 weights and a Q80 buffer. The generation time encompasses the inference time, network transfer time, sampling time, and multi-thread synchronization time. Number of samples: 16.
Raspberry Pi 4B 8 GB
8 x Raspberry Pi 4B 8GB
All Raspberry Pi units were connected via Gigabit Ethernet to the TP-Link LS1008G Switch.
| Model | 1 x RasPi 4B 8 GB | 2 x RasPi 4B 8 GB | 4 x RasPi 4B 8 GB | 8 x RasPi 4B 8 GB |
|---|---|---|---|---|
| Llama 2 7B | 1312.50 ms (I: 1307.94 ms, T: 1.81 ms) |
793.69 ms (I: 739.00 ms, T: 52.50 ms) |
494.00 ms 🔥 (I: 458.81 ms, T: 34.06 ms) |
588.19 ms (I: 296.69 ms, T: 289.75 ms) |
| Llama 2 13B | Not enough RAM | 1497.19 ms (I: 1465.06 ms, T: 30.88 ms) |
848.19 ms 🔥 (I: 746.88 ms, T: 99.50 ms) |
1114.88 ms (I: 460.8 ms, T: 652.88 ms) |
| Llama 2 70B | Not enough RAM | Not enough RAM | Not enough RAM | 4842.81 ms 🔥 (I: 2121.94 ms, T: 2719.62 ms) |
I - inference time of the root node, T - network transfer time
Raspberry Pi 5 8GB
| Model | 1 x RasPi 5 8 GB |
|---|---|
| Llama 2 7B | 436.25 ms (I: 433.31 ms, T: 2.19 ms) by @segabor |
I - inference time of the root node, T - network transfer time
x86_64 CPU Cloud Server
All tests below were conducted on c3d-highcpu-30 (30 vCPU, 15 core, 59 GB memory) VMs in Google Cloud. More details.
| Model | 1 x VM | 2 x VM | 4 x VM |
|---|---|---|---|
| Llama 2 7B | 101.81 ms (I: 101.06 ms, T: 0.19 ms) |
69.69 ms (I: 61.50 ms, T: 7.62 ms) |
53.69 ms 🔥 (I: 40.25 ms, T: 12.81 ms) |
| Llama 2 13B | 184.19 ms (I: 182.88 ms, T: 0.69 ms) |
115.38 ms (I: 107.12 ms, T: 7.81 ms) |
86.81 ms 🔥 (I: 66.25 ms, T: 19.94 ms) |
| Llama 2 70B | 909.69 ms (I: 907.25 ms, T: 1.75 ms) |
501.38 ms (I: 475.50 ms, T: 25.00 ms) |
293.06 ms 🔥 (I: 264.00 ms, T: 28.50 ms) |
I - inference time of the root node, T - network transfer time
F32 Buffer
| Model | 2 devices | 4 devices | 8 devices |
|---|---|---|---|
| Llama 2 7B | 4192 kB (S: 2224 kB, R: 1968 kB) |
10656 kB (S: 7704 kB, R: 2952 kB) |
22624 kB (S: 19180 kB, R: 3444 kB) |
| Llama 2 13B | 6560 kB (S: 3480 kB, R: 3080 kB) |
16680 kB (S: 12060 kB, R: 4620 kB) |
35420 kB (S: 30030 kB, R: 5390 kB) |
| Llama 2 70B |
S - sent data from the root node to workers, R - received data by the root node from workers
Q80 Buffer
| Model | 2 devices | 4 devices | 8 devices |
|---|---|---|---|
| Llama 2 7B | 1112 kB (S: 590 kB, R: 522 kB) |
2830 kB (S: 2046 kB, R: 784 kB) |
6008 kB (S: 5094 kB, R: 914 kB) |
| Llama 2 13B | 1742 kB (S: 924 kB, R: 818 kB) |
4430 kB (S: 3203 kB, R: 1227 kB) |
9407 kB (S: 7976 kB, R: 1431 kB) |
| Llama 2 70B | 5525 kB (S: 3230 kB, R: 2295 kB) |
14917 kB (S: 11475 kB, R: 3442 kB) |
32873 kB (S: 28857 kB, R: 4016 kB) |
S - sent data from the root node to workers, R - received data by the root node from workers
- Install
Raspberry Pi OS Lite (64 bit)on your Raspberry Pi devices. This OS doesn't have desktop environment. - Connect all devices to the Gigabit switch.
- Connect to all devices via SSH.
ssh user@raspberrypi1.local
ssh user@raspberrypi2.local
- Install Git:
sudo apt install git- Clone this repository:
git clone https://github.com/b4rtaz/distributed-llama.git- Compile Distributed Llama:
make main- Transfer weights and the tokenizer file to the root device.
- Optional: assign static IP addresses.
sudo ip addr add 10.0.0.1/24 dev eth0 # 1th device
sudo ip addr add 10.0.0.2/24 dev eth0 # 2th device- Run worker nodes on worker devices:
sudo nice -n -20 ./main worker --port 9998 --nthreads 4- Run root node on the root device:
sudo nice -n -20 ./main inference --model ../dllama_llama-2-7b_q40.bin --tokenizer ../dllama-llama2-tokenizer.t --weights-float-type q40 --buffer-float-type q80 --prompt "Hello world" --steps 16 --nthreads 4 --workers 10.0.0.2:9998To add more worker nodes, just add more addresses to the --workers argument.
./main inference ... --workers 10.0.0.2:9998 10.0.0.3:9998 10.0.0.4:9998
You need to have x86_64 AVX2 CPU or ARM CPU. Different devices may have different CPUs. The below instructions are for Debian-based distributions but you can easily adapt them to your distribution or macOS.
- Install Git and G++:
sudo apt install git build-essential- Clone this repository:
git clone https://github.com/b4rtaz/distributed-llama.git- Compile Distributed Llama:
make main- Transfer weights and the tokenizer file to the root node.
- Run worker nodes on worker devices:
sudo nice -n -20 ./main worker --port 9998 --nthreads 4- Run root node on the root device:
sudo nice -n -20 ./main inference --model ../dllama_llama-2-7b_q40.bin --tokenizer ../dllama-llama2-tokenizer.t --weights-float-type q40 --buffer-float-type q80 --prompt "Hello world" --steps 16 --nthreads 4 --workers 192.168.0.1:9998- To run the root node in the chat mode:
sudo nice -n -20 ./main chat --model ../dllama_llama-2-7b-chat_q40.bin --tokenizer ../dllama-llama2-tokenizer.t --weights-float-type q40 --buffer-float-type q80 --nthreads 4 --workers 192.168.0.1:9998This project is released under the MIT license.
@misc{dllama,
author = {Bartłomiej Tadych},
title = {Distributed Llama},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/b4rtaz/distributed-llama}},
commit = {7eb77ca93ec0d502e28d36b6fb20039b449cbea4}
}
