base on Distributed LLM inference. Connect home devices into a powerful cluster to accelerate LLM inference. More devices means faster inference.  # Distributed Llama [](https://github.com/b4rtaz/distributed-llama/actions) [](/LICENSE) [](https://n4no.com/projects/distributedLlama/discord.php) 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** - [💻 How to Run on Linux, MacOS or Windows](./docs/HOW_TO_RUN_LINUX_MACOS_WIN.md) - [🍓 How to Run on Raspberry Pi](./docs/HOW_TO_RUN_RASPBERRYPI.md) - [🧠 How to Run on GPU](./docs/HOW_TO_RUN_GPU.md) **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 - [🌋 Experimental Vulkan support](https://github.com/b4rtaz/distributed-llama/releases/tag/v0.13.0) - 12 Feb 2025 - 🚧 Merged the [fundamental codebase refactor](https://github.com/b4rtaz/distributed-llama/releases/tag/v0.12.0) - 9 Jan 2025 - [🍎 Llama 3.3 70B on 4 x Mac Mini M4 Pro 24GB RAM](https://github.com/b4rtaz/distributed-llama/discussions/147) ### 🔥 Setup Root Node by Single Command 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` | ### 🛠️ Convert Model Manually * [🤗 How to Convert Hugging Face Model](./docs/HOW_TO_CONVERT_HF_MODEL.md) ### 🚧 Known Limitations * 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](https://github.com/b4rtaz/distributed-llama/issues/70). * Only the following quantizations are supported [#183](https://github.com/b4rtaz/distributed-llama/issues/183): * `q40` model with `q80` `buffer-float-type` * `f32` model with `f32` `buffer-float-type` ### 👷 Architecture ```` [🔀 SWITCH OR ROUTER] | | | | | | | |_______ 🔸 device1 (ROOT) 10.0.0.1 | | |_________ 🔹 device2 (WORKER 1) 10.0.0.2:9999 | |___________ 🔹 device3 (WORKER 2) 10.0.0.3:9999 |_____________ 🔹 device4 (WORKER 3) 10.0.0.4:9999 ... ```` 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. ### 🎹 Commands * `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. <details> <summary>🎹 Supported Arguments</summary> <br />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` | </details> ## 📊 Measurements Please check the [discussions](https://github.com/b4rtaz/distributed-llama/discussions) section, where many measurements were published on different configurations. ## ✋ Contribution 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. ## 💡 License This project is released under the MIT license. ## 📖 Citation ``` @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} } ``` ", Assign "at most 3 tags" to the expected json: {"id":"8300","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"