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LatestNexaAI4B
NexaAIAny-to-Any

NexaAI Releases OmniNeural-4B for On-Device AI

The new 4-billion-parameter model is designed for 'any-to-any' multimodal tasks and optimized to run efficiently on mobile hardware.

Aug 15, 2025
NotableCC BY 4.0
OmniNeural-4B

NexaAI has introduced OmniNeural-4B, a new 4-billion-parameter model designed to bring versatile AI capabilities directly to consumer devices. Its compact size and specific optimizations target the growing demand for powerful, locally-run artificial intelligence.

Unlike models that handle specific input-output pairs like text-to-image, OmniNeural-4B is an 'any-to-any' multimodal system. This architecture allows it to process and generate a wide combination of data types, which could enable more complex and integrated on-device applications.

Designed for the Edge

The core focus of this release is performance on edge devices. NexaAI highlights that OmniNeural-4B is specifically optimized for the Neural Processing Units (NPUs) found in modern Snapdragon chips and Android hardware. This approach bypasses the need for cloud servers, potentially offering faster, more private, and offline-capable AI experiences directly on a user's phone.

The model is available on Hugging Face under a permissive Creative Commons BY 4.0 license, allowing developers and researchers to freely experiment with building sophisticated multimodal features into mobile applications.

Sources

  • NexaAI/OmniNeural-4B

    Hugging Face

    Visit
OlderTencent Releases Controllable Game Video ModelTencent · Image → Video · last yearNewerQwen Releases Open Model for Image EditingQwen · Alibaba · Image Editing · last year

Get the model

Hugging Face

Specs

Parameters4B
LicenseCC-BY-4.0
Downloads1.9K
Likes168

Can you run it?

Runs on a laptop — about 4.2 GB at 4-bit.

  • BF16 (as published)

    12 GB GPU (RTX 3060 / 4070) · 16 GB Mac

    9.8 GB
  • 8-bit

    8 GB GPU (RTX 4060) · 16 GB laptop or Mac

    6 GB
  • 4-bit

    8 GB GPU (RTX 4060) · 16 GB laptop or Mac

    4.2 GB
Your machine
  • Precision not published; assuming 16-bit (BF16) weights.
  • Parameter count is an editorial estimate — no weights metadata on Hugging Face.
  • KV cache approximated from model size (no config metadata) at 8,192 tokens.

Estimated from the parameter count. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Any-to-Any

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