[OpenVINO](https://docs.openvino.ai/) is an open-source toolkit for optimizing and deploying high-performance AI inference, specifically designed for Intel hardware, including CPUs, GPUs, and NPUs, in the cloud, on-premises, and on the edge. [OpenVINO backend for llama.cpp](../../ggml/src/ggml-openvino) enables hardware-accelerated inference on **Intel® CPUs, GPUs, and NPUs** while remaining compatible with the existing **GGUF model ecosystem**. The backend translates GGML compute graphs into OpenVINO graphs and leverages graph compilation, kernel fusion, and device-specific optimizations to improve inference performance on supported Intel hardware.
The OpenVINO backend is implemented in `ggml/src/ggml-openvino` and provides a translation layer for core GGML operations. The OpenVINO backend replaces the standard GGML graph execution path with Intel's OpenVINO inference engine. This approach allows the same GGUF model file to run on Intel CPUs, Intel GPUs (integrated and discrete), and Intel NPUs without changes to the model or the rest of the llama.cpp stack. When a `ggml_cgraph` is dispatched to OpenVINO backend, it:
- Walks the GGML graph and identifies inputs, outputs, weights, and KV cache tensors.
- Translates the GGML operations into an `ov::Model` using OpenVINO's frontend API.
- Compiles and caches the model for the target device.
- Binds GGML tensor memory to OpenVINO inference tensors and runs inference.
## Supported Devices
OpenVINO backend supports the following hardware:
- Intel CPUs
- Intel GPUs (integrated and discrete)
- Intel NPUs
Although OpenVINO supports a wide range of [Intel hardware](https://docs.openvino.ai/2026/about-openvino/release-notes-openvino/system-requirements.html), the llama.cpp OpenVINO backend has been validated specifically on AI PCs such as the Intel® Core™ Ultra Series 1 and Series 2.
## Supported Model Precisions
-`FP16`
-`BF16` (on Intel Xeon)
-`Q8_0`
-`Q4_0`
-`Q4_1`
-`Q4_K`
-`Q4_K_M`
-`Q5_K` (converted to Q8_0_C at runtime)
-`Q6_K` (converted to Q8_0_C at runtime)
> [!NOTE]
> Accuracy validation and performance optimizations for quantized models are a work in progress.
## Quantization Support Details
### CPU and GPU
- **`Q4_0`, `Q4_1`, `Q4_K_M`, `Q6_K` models are supported**
-`Q5_K` and `Q6_K` tensors are converted to `Q8_0_C`
### NPU
- **Primary supported quantization scheme is `Q4_0`**
-`Q6_K` tensors are requantized to `Q4_0_128` in general. For embedding weights, `Q6_K` tensors are requantized to `Q8_0_C` except for the token embedding matrix which is dequantized to fp16
### Additional Notes
- Both `Q4_0` and `Q4_1` models use `Q6_K` for the token embedding tensor and the final matmul weight tensor (often the same tensor)
-`Q4_0` models may produce some `Q4_1` tensors if an imatrix is provided during quantization using `llama-quantize`
-`Q4_K_M` models may include both `Q6_K` and `Q5_K` tensors (observed in Phi-3)
## Validated Models
The following models have been validated for functionality on Intel® Core™ Ultra Series 1 and Series 2:
- Linux or Windows system with Intel hardware (CPU, GPU, or NPU)
- **For Intel GPU or NPU Usage**: Install the appropriate hardware drivers for your Intel GPU or NPU. For detailed instructions, see: [Additional Configurations for Hardware Acceleration](https://docs.openvino.ai/2025/get-started/install-openvino/configurations.html).
- **Linux:**
- Git, CMake, and Ninja software tools are needed for building.
- Download and install [Microsoft Visual Studio 2022 Build Tools](https://aka.ms/vs/17/release/vs_BuildTools.exe). During installation, select the **"Desktop development with C++"** workload.
- Install required tools:
```powershell
# Windows PowerShell
winget install Git.Git
winget install GNU.Wget
winget install Ninja-build.Ninja
```
- Install **OpenCL** using **vcpkg**:
```powershell
# Windows PowerShell
cd C:\
git clone https://github.com/microsoft/vcpkg
cd vcpkg
.\bootstrap-vcpkg.bat
.\vcpkg install opencl
# Optional but recommended: Integrate vcpkg with Visual Studio / CMake:
.\vcpkg integrate install
```
### 1. Install OpenVINO Runtime
- Follow the guide to install OpenVINO Runtime from an archive file: [Linux](https://docs.openvino.ai/2026/get-started/install-openvino/install-openvino-archive-linux.html) | [Windows](https://docs.openvino.ai/2026/get-started/install-openvino/install-openvino-archive-windows.html)
- **Linux:**
<details>
<summary>📦 Click to expand OpenVINO installation from an archive file on Ubuntu</summary>
When using the OpenVINO backend, the first inference token may have slightly higher latency due to on-the-fly conversion to the OpenVINO graph. Subsequent tokens and runs will be faster.
> Default context size is set to the model training context, which may be very large. For example, 131072 for Llama 3.2 1B, which may result in lower performance, especially on edge/laptop devices. Use `-c` to limit context size in supported llama.cpp tools for better performance. For example, `-c 512`.
> On systems with multiple GPUs, use `GPU.0` or `GPU.1` to explicitly target specific GPU. See [OpenVINO GPU Device](https://docs.openvino.ai/2026/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html) for more details.
- GPU stateless execution is currently affected by a known issue.
- Workaround: set `GGML_OPENVINO_STATEFUL_EXECUTION=1` when using GPU device.
- NPU failures can happen when context size is too large. Recent llama.cpp behavior may resolve context size to the model training context (for example, 131072 for Llama 3.2 1B), which is too large for current NPU usage and can also stress laptop CPU/GPU on larger models. To inspect the selected context size, run `llama-cli` or `llama-server` with `-lv 3`.
- Workaround: explicitly set context size, for ex. `-c 1024` for NPU runs. Performance will be better with lower context size.
- Additional NPU limitations:
- Model caching is not yet supported.
- `llama-server -np > 1` (multiple parallel sequences) is not supported.
- `llama-perplexity` is only supported with `-b 512` or smaller.
- `--context-shift` with `llama-cli` is currently not supported with OpenVINO backend across CPU, GPU, and NPU devices.
- Encoder models (embedding, reranking) are not supported with the current OpenVINO backend implementation.
- `-fa 1` is required when running llama-bench with the OpenVINO backend.
curl -X POST "http://localhost:8080/v1/chat/completions" -H "Content-Type: application/json" \
-d '{"messages":[{"role":"user","content":"Write a poem about OpenVINO"}],"max_tokens":100}' | jq .
```
## Runtime Configuration
The OpenVINO backend can be configured using the following environment variables at runtime to control device selection, caching, debugging, and profiling behavior.
| `GGML_OPENVINO_DEVICE` | `CPU` | Specify the target device (CPU, GPU, NPU). On systems with multiple GPUs, use `GPU.0` or `GPU.1` to explicitly target specific GPU. See [OpenVINO GPU Device](https://docs.openvino.ai/2026/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html). When set to **NPU**, static compilation mode is enabled for optimal performance. |
| `GGML_OPENVINO_CACHE_DIR` | `not set` | Directory for OpenVINO model caching (recommended: `/tmp/ov_cache`). Enables model caching when set. **Not supported on NPU devices.** |
>`GGML_OPENVINO_STATEFUL_EXECUTION` is an **Experimental** feature to allow stateful execution for managing the KV cache internally inside the OpenVINO model, improving performance on CPUs and GPUs. Stateful execution is not effective on NPUs, and not all models currently support this feature. This feature is experimental and has been validated only with the llama-simple, llama-cli, llama-bench, and llama-run applications and is recommended to enable for the best performance. Other applications, such as llama-server and llama-perplexity, are not yet supported.
### Example Usage
#### GPU Inference with Profiling
```bash
# If the system has multiple GPUs, use GPU.0 or GPU.1 to explicitly target a specific GPU.
