# Show HN: Jeva.cpp – a llama.cpp fork with JEV-compatible API for all LLMs > Source: > Published: 2026-09-28 14:29:07+00:00 **jeva.cpp** is a fork of llama.cpp that adds a [JEV-compatible decision API](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/jeva.md) to `llama-server`, enabling Choice, Score and Noul evaluations directly from model logits while preserving standard autoregressive generation. jeva.cpp is designed to work with all models and platforms supported by llama.cpp, reusing its existing model implementations and inference backends. The JEV decision API requires models that provide next-token vocabulary logits; other model types retain their original functionality. **LLM inference in C/C++** [ggml](https://github.com/ggml-org/ggml) / [ops](https://github.com/ggml-org/llama.cpp/blob/master/docs/ops.md) / [maintainer PRs]() / [dev stats](https://github.com/ggml-org/llama.cpp-dev) / [lib llama API](https://github.com/ggml-org/llama.cpp/issues/9289) / [llama-server REST API](https://github.com/ggml-org/llama.cpp/issues/9291) A few options to get `llama.cpp` installed on your machine: - Visit [https://llama.app](https://llama.app) and follow the instructions - Run with Docker - see our [Docker documentation](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/docker.md) - Download pre-built binaries from the [releases page](https://github.com/ggml-org/llama.cpp/releases) - Build from source by cloning this repository - check out [our build guide](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/build.md) Once installed: ``` # Download and run a model directly from Hugging Face llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF # Launch OpenAI-compatible API server llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF ``` | *VLM session with **llama cli*** | *Built-in web UI against **llama serve*** | The main goal of `llama.cpp` is to enable LLM (and VLM) inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud. - Plain C/C++ implementation without any dependencies - Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks - AVX, AVX2, AVX512 and AMX support for x86 architectures - RVV, ZVFH, ZFH, ZICBOP and ZIHINTPAUSE support for RISC-V architectures - 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use - Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads GPUs via MUSA) - Vulkan and SYCL backend support - CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity The `llama.cpp` project is build on top of the [ggml](https://github.com/ggml-org/ggml) library. | Backend | Target devices | |---|---| | [BLAS](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/build.md#blas-build) | All | | [BLIS](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/backend/BLIS.md) | All | | [CANN](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/build.md#cann) | Ascend NPU | | [CUDA](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/build.md#cuda) | Nvidia GPU | | [HIP](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/build.md#hip) | AMD GPU | | [Hexagon](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/backend/snapdragon/README.md) | Snapdragon | | [IBM zDNN](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/backend/zDNN.md) | IBM Z & LinuxONE | | [MUSA](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/build.md#musa) | Moore Threads GPU | | [Metal](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/build.md#metal-build) | Apple Silicon | | [OpenCL](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/backend/OPENCL.md) | Adreno GPU | | [OpenVINO \[In Progress\]](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/backend/OPENVINO.md) | Intel CPUs, GPUs, and NPUs | | [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All | | [SYCL](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/backend/SYCL.md) | Intel GPU | | [VirtGPU](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/backend/VirtGPU.md) | VirtGPU APIR | | [Vulkan](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/build.md#vulkan) | GPU | | [WebGPU](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/build.md#webgpu) | All | | [ZenDNN](https://github.com/PragmaTwice/jeva.cpp/blob/master/docs/build.md#zendnn) | AMD CPU | - Contributors can open PRs - Collaborators will be invited based on contributions - Maintainers can push to branches in the `llama.cpp` repo and merge PRs into the`master` branch - Any help with managing issues, PRs and projects is very appreciated! - Read the [CONTRIBUTING.md](https://github.com/PragmaTwice/jeva.cpp/blob/master/CONTRIBUTING.md) for more information - [yhirose/cpp-httplib](https://github.com/yhirose/cpp-httplib) - Single-header HTTP server, used by`llama-server` - MIT license - [nothings/stb](https://github.com/nothings/stb) - Single-header image format decoder, used by multimodal subsystem - Public domain - [nlohmann/json](https://github.com/nlohmann/json) - Single-header JSON library, used by various tools/examples - MIT License - [mackron/miniaudio](https://github.com/mackron/miniaudio) - Single-header audio format decoder, used by multimodal subsystem - Public domain - [sheredom/subprocess.h](https://github.com/sheredom/subprocess.h) - Single-header process launching solution for C and C++ - Public domain