Mastering Local Inference: Building Private AI Apps using Nexa SDK and Go

NexaAI/nexa-sdk

In a world where most AI relies on expensive cloud APIs, this SDK allows you to run powerful models (like LLMs and Vision Language Models) directly on your user's hardware—whether that’s a high-end PC or a mobile phone.

Here is a breakdown of why this is a game-changer and how you can get started.

As engineers, we usually face a trade-off
Cloud APIs (easy but expensive and privacy-heavy) vs. Local Execution (fast and private but a nightmare to optimize for different hardware). Nexa SDK solves this by

Cross-Platform Hardware Acceleration
It doesn't just run on CPUs. It taps into GPUs and NPUs (Neural Processing Units) across Windows, macOS, Linux, Android, and iOS.

Unified Interface
You get a consistent API experience (similar to OpenAI’s format) while switching between very different models like Gemma 3 or Qwen2-VL.

Edge Computing
It’s perfect for apps that need to work offline or require ultra-low latency without data ever leaving the device.

Since you mentioned [go, sdk, llama], I’ll focus on the local environment setup. Nexa provides a powerful CLI and a C++ core that powers its language bindings.

The easiest way to manage models is via their tool

pip install nexa-sdk

You can pull a model (like a quantized Llama 3) directly from their hub

nexa run llama3

While the core is built in C++/Python, Go developers can interact with Nexa-powered models through its Local Server mode. This is a common pattern
Nexa runs a local high-performance inference engine, and your Go backend talks to it via standard HTTP/JSON.

nexa server llama3

This starts a local endpoint at http://localhost:8080/v1 that is compatible with the OpenAI API specification.

Here is how you would consume this in a Go application

package main

import (
	"bytes"
	"encoding/json"
	"fmt"
	"net/http"
)

func main() {
	url := "http://localhost:8080/v1/chat/completions"
	
	// Define the request based on OpenAI-compatible schema
	payload := map[string]interface{}{
		"model": "llama3",
		"messages": []map[string]string{
			{"role": "user", "content": "Explain Go routines like I'm five."},
		},
	}

	jsonData, _ := json.Marshal(payload)
	resp, err := http.Post(url, "application/json", bytes.NewBuffer(jsonData))
	
	if err != nil {
		fmt.Printf("Error: %s\n", err)
		return
	}
	defer resp.Body.Close()

	// Parse and print the local model's response
	var result map[string]interface{}
	json.NewDecoder(resp.Body).Decode(&result)
	fmt.Println(result["choices"])
}

One of the coolest features is the VLM (Vision Language Model) support. You can feed an image into a model like Qwen3-VL on a mobile device to "see" and describe the world. Nexa handles the heavy lifting of converting these large models into formats optimized for mobile NPUs.

Privacy-First Apps
Build a journaling app where the AI analyzes thoughts locally.

Game Dev
Use local LLMs for NPC dialogue without worrying about a monthly API bill.

Industrial IoT
Use the NPU support on edge devices to detect anomalies in images without internet.