
distributed-llama
Distributed LLM inference. Connect home devices into a powerful cluster to accelerate LLM inference. More devices means faster inference.
Stars: 2651

Distributed Llama is a tool that allows you to run large language models (LLMs) on weak devices or make powerful devices even more powerful by distributing the workload and dividing the RAM usage. It uses TCP sockets to synchronize the state of the neural network, and you can easily configure your AI cluster by using a home router. Distributed Llama supports models such as Llama 2 (7B, 13B, 70B) chat and non-chat versions, Llama 3, and Grok-1 (314B).
README:
Connect home devices into a powerful cluster to accelerate LLM inference. More devices mean faster performance, leveraging tensor parallelism and high-speed synchronization over Ethernet.
Supports Linux, macOS, and Windows. Optimized for ARM and x86_64 AVX2 CPUs.
How to Run
News
- 16 Sep 2025 - Qwen 3 MoE models are now supported on Vulkan.
- 5 Sep 2025 - Qwen 3 MoE models are now supported on CPU.
- 3 Aug 2025 - Qwen 3 0.6B, 1.7B, 8B and 14B models are now supported.
- 23 Mar 2025 - π Experimental Vulkan support
- 12 Feb 2025 - π§ Merged the fundamental codebase refactor
- 9 Jan 2025 - π Llama 3.3 70B on 4 x Mac Mini M4 Pro 24GB RAM
Python 3 and C++ compiler required. The command will download the model and the tokenizer.
Model | Size | Command |
---|---|---|
Llama 3.1 8B Instruct Q40 | 6.32 GB | python launch.py llama3_1_8b_instruct_q40 |
Llama 3.1 405B Instruct Q40 | 238 GB |
python launch.py llama3_1_405b_instruct_q40 . |
Llama 3.2 1B Instruct Q40 | 1.7 GB | python launch.py llama3_2_1b_instruct_q40 |
Llama 3.2 3B Instruct Q40 | 3.4 GB | python launch.py llama3_2_3b_instruct_q40 |
Llama 3.3 70B Instruct Q40 | 40 GB | python launch.py llama3_3_70b_instruct_q40 |
DeepSeek R1 Distill Llama 8B Q40 | 6.32 GB | python launch.py deepseek_r1_distill_llama_8b_q40 |
Qwen 3 0.6B Q40 | 0.9 GB | python launch.py qwen3_0.6b_q40 |
Qwen 3 1.7B Q40 | 2.2 GB | python launch.py qwen3_1.7b_q40 |
Qwen 3 8B Q40 | 6.7 GB | python launch.py qwen3_8b_q40 |
Qwen 3 14B Q40 | 10.9 GB | python launch.py qwen3_14b_q40 |
Qwen 3 30B A3B Q40 | 17.0 GB | python launch.py qwen3_30b_a3b_q40 |
- You can run Distributed Llama only on 1, 2, 4... 2^n nodes.
- The maximum number of nodes is equal to the number of KV heads in the model #70.
- Only the following quantizations are supported #183:
-
q40
model withq80
buffer-float-type
-
f32
model withf32
buffer-float-type
-
[π SWITCH OR ROUTER]
| | | |
| | | |_______ πΈ device1 (ROOT) 10.0.0.1
| | |_________ πΉ device2 (WORKER 1) 10.0.0.2:9999
| |___________ πΉ device3 (WORKER 2) 10.0.0.3:9999
|_____________ πΉ device4 (WORKER 3) 10.0.0.4:9999
...
The project is split up into two parts:
- πΈ 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.
- πΉ 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.
-
dllama inference
- run the inference with a simple benchmark, -
dllama chat
- run the CLI chat, -
dllama worker
- run the worker node, -
dllama-api
- run the API server.
πΉ Supported Arguments
Inference, Chat, API
Argument | Description | Example |
---|---|---|
--model <path> |
Path to model. | dllama_model_meta-llama-3-8b_q40.m |
--tokenizer <path> |
Tokenizer to model. | dllama_tokenizer_llama3.t |
--buffer-float-type <type> |
Float precision of synchronization. | q80 |
--workers <workers> |
Addresses of workers (ip:port), separated by space. | 10.0.0.1:9999 10.0.0.2:9999 |
--max-seq-len <n> |
The maximum sequence length, it helps to reduce the RAM usage. | 4096 |
Inference, Chat, Worker, API
Argument | Description | Example |
---|---|---|
--nthreads <n> |
Amount of threads. Don't set a higher value than number of CPU cores. | 4 |
Worker, API
Argument | Description | Example |
---|---|---|
--port <port> |
Binding port. | 9999 |
Inference
Argument | Description | Example |
---|---|---|
--prompt <prompt> |
Initial prompt. | "Hello World" |
--steps <steps> |
Number of tokens to generate. | 256 |
Please check the discussions section, where many measurements were published on different configurations.
Feel free to contribute to this project. For small changes, simply create a new merge request. For larger changes, please create an issue to discuss your plans. Please follow these guidelines when contributing:
- Make only minimal changes and avoid modifying files that are not necessary.
- Ensure the code is compatible across all supported systems and CPUs.
- This repository is maintained in English.
This project is released under the MIT license.
@misc{dllama,
author = {BartΕomiej Tadych},
title = {Distributed Llama},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/b4rtaz/distributed-llama}},
commit = {7eb77ca93ec0d502e28d36b6fb20039b449cbea4}
}
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