Nexa SDK

Nexa SDK is hands down one of the most versatile and forward-thinking AI runtimes I’ve worked with. The biggest win for me is how it abstracts away the complexity of hardware and backend differences—you can literally run any model on any device locally, whether it’s text, vision, audio, speech, or even image generation. Performance-wise, it doesn’t lock you into one vendor. Nexa supports Qualcomm and Apple NPUs out of the box, alongside GPU and CPU acceleration. For developers, that’s huge—it means you can target mobile, desktop, and edge devices without rewriting or restructuring your code. The model compatibility is equally impressive. Nexa handles GGUF, Apple MLX, and integrates the latest SOTA models like Gemma3n and PaddleOCR seamlessly. I tested it with both large LLMs and lightweight OCR pipelines, and the consistency in performance was remarkable. What stands out most is the future-proofing: Nexa isn’t just tied to today’s models and backends—it’s clearly designed to adapt as new hardware and architectures roll out. That’s critical if you don’t want to be locked into a narrow ecosystem.

As someone running models on my Mac, I love seeing local-first done right—Apple MLX support plus solid on-device performance is exactly what I want. Lower latency, better privacy, no surprise cloud bills. This makes iterating on ideas way faster.

Nexa SDK makes running local AI incredibly easy. I love how it supports text, vision, audio, and image models on any device — whether CPU, GPU, or NPU. The flexibility with Apple and Qualcomm support plus SOTA models is a huge win. Perfect for building and shipping AI fast without the cloud overhead.