Most local-inference tools — Ollama, llama.cpp, LM Studio — treat the GPU as the finish line and leave the NPU idle, even though the NPU is the chip purpose-built for low-power neural math and already ships in almost every recent phone and laptop. NexaSDK's wager is that the real frontier for on-device AI isn't the GPU but the NPU, and it gets there through a custom inference engine (NexaML) rather than as another llama.cpp wrapper.
What Sets It Apart
- NPU as a first-class target. Native Qualcomm Hexagon NPU execution lets models run faster and at lower power than CPU/GPU fallbacks — often the difference between a demo and something that survives a phone battery.
- Day-0 coverage of new models. Releases like Qwen3-VL, Gemma3n, and DeepSeek-OCR land in days rather than the weeks competitors typically take, so you aren't blocked waiting on community quantization work.
- One runtime, many modalities. A single tool handles text LLMs, vision-language, ASR, OCR, object detection, image generation, and embeddings instead of stitching together a separate stack per task.
- Drop-in surface. Single-command inference plus OpenAI-compatible APIs let existing app code point at a local endpoint with minimal changes.
Who It's For
Great fit if you ship AI onto Android, Windows, Linux, or IoT hardware and want to exploit the NPU instead of pinning everything to a GPU, or if you need the newest models running locally the week they drop. Look elsewhere if you only run server-side inference, target Apple Silicon as your primary platform, or just want the simplest GGUF-on-GPU experience — there the maturity of Ollama or llama.cpp may serve you better. Note the split license: CPU/GPU components are BSD 3-Clause, while NPU components require a free personal or paid commercial agreement.
