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Ensure that your JAVA_HOME points to a supported JDK before using the SDK. Download an SDK package matching your OS, architecture, and accelerator backend (opencl, ptx).
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All pre-built SDKs are available on the TornadoVM [Releases Page](https://github.com/beehive-lab/TornadoVM/releases).
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#After extracting the SDK, add its bin/ directory to your PATH so the `tornado` command becomes available.
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TornadoVM is distributed through our [**official website**](https://www.tornadovm.org/downloads) and **SDKMAN!**. Install a version that matches your OS, architecture, and accelerator backend.
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##### Linux (x86_64)
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All TornadoVM SDKs are available on the [SDKMAN! TornadoVM page](https://sdkman.io/sdks/tornadovm/).
# Source the project-specific environment paths -> this will ensure the correct paths are set for the project and the TornadoVM SDK
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# Expect to see: [INFO] Environment configured for Llama3 with TornadoVM at: $TORNADO_SDK
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source set_paths
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# Build the project using Maven (skip tests for faster build)
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# mvn clean package -DskipTests or just make
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make
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# Run the model (make sure you have downloaded the model file first - see below)
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./llama-tornado --gpu --verbose-init --opencl --model beehive-llama-3.2-1b-instruct-fp16.gguf --prompt "tell me a joke"
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tornado --devices
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```
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----------
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### TornadoVM-Accelerated Inference Performance and Optimization Status
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We are at the early stages of Java entering the AI world with features added to the JVM that enable faster execution such as GPU acceleration, Vector acceleration, high-performance access to off-heap memory and others.
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<br><br>This repository provides the first Java-native implementation of Llama3 that automatically compiles and executes Java code on GPUs via TornadoVM.
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The baseline numbers presented below provide a solid starting point for achieving more competitive performance compared to llama.cpp or native CUDA implementations.
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[Our roadmap](https://github.com/beehive-lab/GPULlama3.java/blob/main/docs/GPULlama3_ROADMAP.md) provides the upcoming set of features that will dramatically improve the numbers below with the clear target being to achieve performance parity with the fastest implementations.
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<br><br>
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If you achieve additional performance data points (e.g. new hardware or platforms) please let us know to add them below.
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<br><br>
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In addition, if you are interested to learn more about the challenges of managed programming languages and GPU acceleration, you can read [our book](https://link.springer.com/book/10.1007/978-3-031-49559-5) or consult the [TornadoVM educational pages](https://www.tornadovm.org/resources).
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