NexaSDK: Running Any AI Model on Any Hardware Has Never Been Easier

Have you ever wanted to run the latest large AI models on your own computer, only to be deterred by complex configuration and hardware compatibility issues? Or perhaps you own a device with a powerful NPU (Neural Processing Unit) but struggle to find AI tools that can fully utilize its capabilities? Today, we introduce a tool that might change all of that: NexaSDK.

Imagine a tool that lets you run thousands of AI models from Hugging Face locally with a single line of code, capable of handling text, understanding images, or generating speech. More importantly, it’s not picky—whether you have an Apple M-series chip, a Qualcomm Snapdragon X Elite, an Intel Core Ultra, or a standard GPU and CPU, it can maximize your hardware’s potential. This is the experience NexaSDK aims to deliver.

The Challenges of Local AI Deployment and a New Solution

Before diving into NexaSDK, let’s consider the current landscape. As AI models grow larger and more capable, running them in the cloud is convenient, but it brings issues like latency, privacy, cost, and network dependency. Therefore, running AI models on local devices has become a pressing need for many developers, researchers, and tech enthusiasts.

However, the path isn’t smooth. You might encounter several typical problems:

• Hardware Fragmentation: My computer has an NPU, but do mainstream AI frameworks support it? Can my Apple chip run this model?
• Model Format Confusion: GGUF, MLX, PyTorch… Which version should I download? Will this format run efficiently on my device?
• Complex Deployment: After downloading the model files, you still need to configure the environment, install dependencies, and tweak parameters—a tedious and time-consuming process.
• Performance Bottlenecks: The model might run, but it’s slow, power-hungry, and impractical for real use.

Several excellent tools already address parts of this problem, such as llama.cpp for CPU inference, the user-friendly Ollama, and the GUI-based LM Studio. However, they often have limitations, particularly in supporting emerging NPU hardware, cross-platform capabilities, and unified support for multimodal models.

NexaSDK emerges as a unified, hardware-agnostic, and extremely developer-friendly solution. At its core is a low-level inference engine called NexaML. Unlike tools that are “wrappers” around existing runtimes, NexaML is rebuilt from the kernel level. This gives it two major advantages: First, it can be deeply optimized for each type of hardware (NPU, GPU, CPU) to achieve peak performance. Second, it can quickly adapt to new model architectures, providing “Day-0” support.

The Core Advantage of NexaSDK: A Clear Comparison

Actions speak louder than words. How does NexaSDK differ from other popular tools? The table below clearly outlines the key distinctions.

Note: ✅ indicates full support; ⚠️ indicates partial or limited support; ❌ indicates no support.

The table shows that NexaSDK has significant advantages in breadth of hardware support, platform coverage, and model format inclusivity. Its NPU-first strategy is particularly noteworthy, allowing users to truly unlock the potential of dedicated AI hardware in their phones and laptops for faster speeds and lower power consumption.

More Than Promises: NexaSDK’s Recent Achievements

The strength of a framework is proven by what it can actually do. A series of recent achievements by the NexaSDK team demonstrates its technical prowess and “Day-0” support capability.

• Proprietary Model Release: Launched AutoNeural-VL-1.5B, an NPU-native vision-language model built for real-time in-car assistants. On the high-end automotive chip Qualcomm SA8295P, it achieves 14x lower latency, 3x faster generation, and 4x longer context length, and it also runs on Snapdragon X Elite laptops.
• Broad Model Support: Successfully ran models like Mistral AI’s Ministral-3-3B across various hardware types.
• Rapid Ecosystem Expansion:

  • Apple Ecosystem: Optimized models like Granite-4.0, Qwen3, Gemma3, and Parakeetv3 for the Apple Neural Engine (ANE).
  • Android Ecosystem: Released a full Android SDK supporting model execution on mobile NPUs.
  • Linux Ecosystem: Launched a Linux SDK for server and edge computing scenarios.
  • AMD Ecosystem: Partnered with AMD to enable SDXL-turbo image generation on AMD NPUs.
• Industry Benchmark Collaborations: Partnered with top model providers for launch-day support:

  • Qwen3-VL: First to support its 4B and 8B versions in GGUF, MLX, and .nexa formats, and the only framework supporting its GGUF format.
  • IBM Granite 4.0: Its NexaML engine was listed alongside vLLM, llama.cpp, and MLX as a recommended inference solution in IBM’s official blog.
  • Google EmbeddingGemma: Received a recommendation from Google’s official social media.

These achievements show that NexaSDK is not just theoretical but an active, rapidly iterating technology platform recognized by major industry players.

🤝 Supported by Leading Chipmakers

How to Get Started with NexaSDK: From Download to Chat in Two Steps

Enough theory. Let’s get hands-on. Running a large AI model locally with NexaSDK is surprisingly simple.

Step 1: Download the Nexa Command-Line Tool with One Click

Choose the appropriate link to download and install based on your operating system and hardware.

macOS Users:

• If you have an Apple Silicon chip (M1/M2/M3, etc.) and want to use the Apple Neural Engine: Download the arm64 ANE version
• If you have an Apple Silicon chip and want to use the MLX backend: Download the arm64 universal version
• If you have an Intel chip (x86_64): Download the x86_64 version

Windows Users:

• If your device has an ARM chip like Snapdragon X Elite and you want NPU support: Download the Windows ARM64 version
• If your device has an Intel or AMD chip and you want NPU/GPU support: Download the Windows x86_64 version

Linux Users:
Execute the corresponding command in your terminal to install.
For x86_64 architecture machines:

curl -fsSL https://github.com/NexaAI/nexa-sdk/releases/latest/download/nexa-cli_linux_x86_64.sh -o install.sh && chmod +x install.sh && ./install.sh && rm install.sh

For arm64 architecture machines (like Raspberry Pi, some servers):

curl -fsSL https://github.com/NexaAI/nexa-sdk/releases/latest/download/nexa-cli_linux_arm64.sh -o install.sh && chmod +x install.sh && ./install.sh && rm install.sh

Step 2: Run a Model with One Line of Code

After installation, open your terminal or command prompt. The basic command format is: nexa infer <model repository name on Hugging Face>.

NexaSDK primarily supports three model formats for different scenarios:

1. Running GGUF Format Models (Most Universal)
GGUF format models can run on CPU and GPU across macOS, Linux, and Windows. NexaSDK’s support for the GGUF format of complex models (like Qwen3-VL) is even unique.

• Run a pure text chat model, like the compact Qwen3-1.7B:

  nexa infer ggml-org/Qwen3-1.7B-GGUF
  

• Run a multimodal vision model, like the image-understanding Qwen3-VL-4B:

  nexa infer NexaAI/Qwen3-VL-4B-Instruct-GGUF
  

2. Running MLX Format Models (Apple Silicon Exclusive)
MLX is Apple’s framework built for its chips and only runs on Apple Silicon devices. Note that NexaAI recommends getting models from their curated model collection for best compatibility and quality.

• Run an MLX format text model:

  nexa infer NexaAI/Qwen3-4B-4bit-MLX
  

• Run an MLX format multimodal model:

  nexa infer NexaAI/gemma-3n-E4B-it-4bit-MLX
  

3. Running NPU-Optimized Models (Using Qualcomm as an Example)
If you want to experience blazing-fast inference on the NPU of a Snapdragon X Elite laptop, you first need to install the corresponding Windows ARM64 client.

• First, obtain a license (for Pro Models):

  1. Register an account at sdk.nexa.ai.
  2. Go to Deployment → Create Token to create a token.
  3. Configure the token in your terminal:

     nexa config set license ‘your_token_here’
     

• Then, run an NPU-optimized model:

  nexa infer NexaAI/OmniNeural-4B
  # or
  nexa infer NexaAI/Granite-4-Micro-NPU
  

After executing the command, the tool will automatically download the model (if not cached) and launch an interactive chat interface. A very cool feature is: when chatting with multimodal models, you can directly drag and drop image or audio files into the terminal window—it even supports dropping multiple images at once!

Advanced Usage and Frequently Asked Questions (FAQ)

Once you’ve mastered the basics, you might want to know more. Here are some advanced commands and answers to common questions.

Essential Command Reference

How to Import Locally Existing Model Files?

If you’ve already downloaded a model via other means (e.g., huggingface-cli), you can load it by specifying the local path:

nexa pull <model_name> --model-hub localfs --local-path /your/model/path

Frequently Asked Questions

Q: What specific AI models does NexaSDK support?
A: Support is very broad, including but not limited to: Meta’s Llama series, Alibaba’s Qwen series (including VL vision models), Google’s Gemma series, IBM’s Granite series, Mistral’s Ministral series, and NexaAI’s proprietary models like OmniNeural and AutoNeural. You can search for models tagged with “GGUF,” “MLX,” or “NPU” on Hugging Face, or visit the official NexaAI model collection.

Q: What hardware do I need to use NPU acceleration?
A: It depends on the NPU type:

• Apple Neural Engine (ANE): Requires a Mac with an Apple Silicon chip (M1/M2/M3, etc.) and the corresponding ANE version client.
• Qualcomm NPU: Requires a Windows on ARM device or Android phone with a Snapdragon X Elite or Snapdragon 8 series platform.
• Intel NPU: Requires a computer with an Intel Core Ultra series processor (e.g., Ultra 5/7/9).
• AMD NPU: Requires a computer with an AMD Ryzen 7040/8040 series or newer APU.
  After installing the corresponding NexaSDK client, the tool will automatically attempt to utilize the available NPU.

Q: Is NexaSDK free?
A: According to official information, basic features and using community models are free. For some “Pro Models” or advanced features, you may need to obtain a license token via the official website (sdk.nexa.ai).

Q: Are there other ways to use it besides the command line?
A: Yes. Besides the CLI, NexaSDK also provides:

1. An OpenAI-compatible API server (nexa serve), allowing you to call models via HTTP requests with your own code.
2. A native Android SDK for integration into mobile applications.
3. An Android Python SDK for calling models via Python scripts on Android devices.

Q: What if the model I want isn’t supported yet?
A: NexaSDK has established a Nexa Wishlist. You can visit this page to submit or vote for models you’d like to see supported. Models with high community demand are prioritized for adaptation and support.

Join the Community and the Builder Bounty Program

Behind NexaSDK is an active open-source community. You can join them in the following ways:

• Discord: Join the Nexa AI Discord to chat with developers and users in real-time.
• Slack: Join the Nexa AI Slack.
• X (Twitter): Follow @nexa_ai for the latest updates.
• Documentation: Detailed usage and development documentation is available at docs.nexa.ai.

More interestingly, NexaAI has launched a Builder Bounty Program. You can earn rewards by building interesting applications based on NexaSDK, with bounties up to $1,500. Details can be found in the Participant Details.

Conclusion

The emergence of NexaSDK signifies that local AI deployment is moving towards a simpler, more unified, and more efficient future. It attempts to solve multiple challenges developers face with one toolkit: hardware compatibility, model format differences, and deployment complexity. Whether you are a researcher looking to quickly validate model performance, a developer hoping to add local AI capabilities to an application, or simply a geek curious about cutting-edge technology, NexaSDK is worth spending a few minutes to try following the “two-step” process outlined here.

After all, the experience of chatting with the latest large model locally using just one line of code is enough to make you feel the pulse of technological progress.