BUBUFU·Jul 7, 2026Submission
Qualcomm acquires Nexa AI, open-sources GenAI runtime for Hexagon NPUs
5 points · Open discussionqualcomm
GenieX
Go2024–2026
qualcomm / GenieX
GenieX
Run frontier LLMs and VLMs locally on Qualcomm devices across NPU, GPU, and CPU with a few lines of code
7Distinct people
11Mentions
0Explicit recommenders
8.5KGitHub stars
What to know before exploring
What it is
Run frontier LLMs and VLMs locally on Qualcomm devices across NPU, GPU, and CPU with a few lines of code
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by month11 dated mentions
Observed mentions: 2024-09-08 18:03:49 UTC to 2026-07-07 22:44:11 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Sep 2024 | 2024-09-01 00:00:00 UTC | 2024-10-01 00:00:00 UTC | 6 | 3 | 3 |
| Oct 2024 | 2024-10-01 00:00:00 UTC | 2024-11-01 00:00:00 UTC | 3 | 2 | 1 |
| Nov 2024 | 2024-11-01 00:00:00 UTC | 2024-12-01 00:00:00 UTC | 1 | 1 | 0 |
| Dec 2024 | 2024-12-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2025 | 2025-01-01 00:00:00 UTC | 2025-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2025 | 2025-02-01 00:00:00 UTC | 2025-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2025 | 2025-03-01 00:00:00 UTC | 2025-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2025 | 2025-04-01 00:00:00 UTC | 2025-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2025 | 2025-05-01 00:00:00 UTC | 2025-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2025 | 2025-06-01 00:00:00 UTC | 2025-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2025 | 2025-07-01 00:00:00 UTC | 2025-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2025 | 2025-08-01 00:00:00 UTC | 2025-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2025 | 2025-09-01 00:00:00 UTC | 2025-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2025 | 2025-10-01 00:00:00 UTC | 2025-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2025 | 2025-11-01 00:00:00 UTC | 2025-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2025 | 2025-12-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2026 | 2026-01-01 00:00:00 UTC | 2026-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2026 | 2026-02-01 00:00:00 UTC | 2026-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2026 | 2026-03-01 00:00:00 UTC | 2026-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2026 | 2026-04-01 00:00:00 UTC | 2026-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2026 | 2026-05-01 00:00:00 UTC | 2026-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2026 | 2026-06-01 00:00:00 UTC | 2026-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2026 | 2026-07-01 00:00:00 UTC | 2026-08-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
11 sourcesBenchmark GGUF models with a one line of code
1 points · Open discussionBenchmark GGUF models with a ONE line of code
1 points · Open discussionBenchmark GGUF model with ONE line of code
6 points · Open discussionHi Everyone!
We built an open-sourced tool to benchmark GGUF models with a single line of code. GitHub Link: https://github.com/NexaAI/nexa-sdk/tree/main/nexa/eval
Motivations:
GGUF quantization is crucial for running models locally on devices, but quantizations can dramatically affect model's performance. It's essential to test models post-quantization (how benchmark comes in clutch). But we noticed a couple of challenges:
1. No easy, fast way to benchmark quantized GGUF models locally or on self-hosted servers.
2. GGUF quantization evaluation results in the existing benchmarks are inconsistent, showing lower scores than the official results from model developers.
Our Solution - We built a tool that:
1. Benchmarks GGUF models with one line of code.
2. Supports multiprocessing and 8 evaluation tasks.
3. In our testing, it's the fastest be
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nchmark for GGUF models available.
Example:
Type below in terminal to benchmark Llama3.2-1B-Instruct Q4_K_M quant on the "ifeval" dataset for general language understanding. It took 80 minutes on a 4090 with 4 workers for multiprocessing.
nexa eval Llama3.2-1B-Instruct:q4_K_M --tasks ifeval --num_workers 4
We started with text models and plan to expand to more on-device models and modalities. Your feedback is welcome! If you find this useful, feel free to let us know on GitHub: https://github.com/NexaAI/nexa-sdk/tree/main/nexa/eval
Llama3.2 3B feels a lot better than other models with same size (e.g. Gemma2, Phi3.5-mini models).
For anyone looking for a simple way to test Llama3.2 3B locally with UI, Install nexa-sdk(https://github.com/NexaAI/nexa-sdk) and type in terminal:
nexa run llama3.2 --streamlit
Disclaimer: I am from Nexa AI and nexa-sdk is an open-sourced. We'd love your feedback.
In Llama 3.2: Revolutionizing edge AI and vision with open, customizable modelsShow HN: I built a Python script uses AI to organize files, runs 100% locally
I wanted a file management tool that actually understands what my files are about. Previous projects like LlamaFS (https://github.com/iyaja/llama-fs) aren't 100% local and require an AI API. So, I created a Python script that leverages AI to organize local files, running entirely on your device for complete privacy. It uses Google Gemma2 2B and llava-v1.6-vicuna-7b models for processing.
Note: You won't need any API key and internet connection to run this project, it runs models entirely on your device.
What it does:
- Scans a specified input directory for files
- Understands the content of your files (text, images, and more) to generate relevant descriptions, folder names, and filenames
- Organizes the files into a new directory structure based on the generated metadata
Supported file types:
- Images: .png, .jpg, .jpeg, .gif,
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.bmp
- Text Files: .txt, .docx
- PDFs: .pdf
Supported systems:
macOS, Linux, Windows
It's fully open source.
For demo & installation guide - GitHub: (https://github.com/QiuYannnn/Local-File-Organizer)
Shoutout to Nexa SDK (https://github.com/NexaAI/nexa-sdk). I discovered it on Reddit and it made this project possible by allowing LM and VLM running entirely on local devices.
What do you think about this project? Is there anything you would like to see in the future version?
Thank you!
For anyone looking for a simple alternative for running local models beyond just text, Nexa AI has built an SDK that supports text, audio (STT, TTS), image generation (e.g., Stable Diffusion), and multimodal models! It also has a model hub to help you easily find local models optimized for your device.
Nexa AI local model hub: https://nexaai.com/
Toolkit: https://github.com/NexaAI/nexa-sdk
It also comes with a built-in local UI to get started with local models easily and OpenAI-compatible API (with JSON schema for function calling and streaming) for starting local development easily.
You can run the Nexa SDK on any device with a Python environment—and GPU acceleration is supported!
Local LLMs, and especially multimodal local models are the future. It is the only way to make AI accessible (cost-efficient) and safe.
In Forget ChatGPT: why researchers now run small AIs on their laptopsRun Qwen 2.5 Family and Other LMs in GGUF Format from HF Locally
11 points · Open discussionHere's some demos we built with the Nexa SDK: (https://github.com/NexaAI/nexa-sdk/tree/main/examples)
We're also planning to have an app gallery where developers can upload their on-device AI apps built with the Nexa SDK. We'll aim to make it as out-of-the-box as possible for others to download and run on their devices.
In Show HN: Nexa SDK – Build powerful and efficient AI apps on edge devicesShow HN: Nexa SDK – Build powerful and efficient AI apps on edge devices
Hey HN! Alex and Zack here from Nexa AI. We're excited to share something we've been working on.
Our journey began with the Octopus series --- action models for mobile AI agents (https://huggingface.co/NexaAIDev/Octopus-v2). We focused on making sub-billion parameter models excel at function calling, making high accurate and fast function-calling possible on mobile and edge devices. But as we delved into developing full-fledged on-device applications, we hit a roadblock.
We realized that optimizing for function calling (tool-use) alone wasn't enough. Building powerful on-device AI apps requires a diverse set of tools: language models with domain expertise, speech processing, image generation, embedding models and more. That's when we decided to create Nexa SDK --- a comprehensive toolkit that brings together everything developers need to
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build powerful and efficient AI applications that run entirely on-device.
Here's what Nexa SDK offers:
- Support for both ONNX and GGML models.
- An integrated conversion engine for making custom GGML Quantized Models for different device hardware requirements.
- An inference engine that supports language models, image generation models, TTS, audio generation models, and Vision-Language Models.
- An OpenAI-compatible API server with optimization in function calling.
- A Streamlit UI for rapid prototyping.
- An intuitive CLI for easy model management.
- Backend optimizations for latency and power consumption on edge devices.
We've designed Nexa SDK to be the go-to solution for developers pushing the boundaries of what's possible with on-device AI applications and AI on edge devices.
To showcase its capabilities, we've built several demo apps running entirely on your device (https://github.com/NexaAI/nexa-sdk/tree/main/examples):
- AI soulmate with uncensored model and audio-in/audio-out interaction.
- A quick interface for uploading and chatting with PDFs like your personal finance documents.
- A meeting transcription app supporting multiple languages and real-time translation.
We're proud to share that the winner of yesterday's (Sep 7) House AGI "AI PC/ GenAI Goes Local" hackathon used Nexa SDK to build a local semantic image search (https://github.com/asl3/deja-view).
But we're just getting started! There are lots of exciting developments in our pipeline, and we can't wait to share them with you soon!
Check it out: (https://github.com/NexaAI/nexa-sdk)
Docs: (https://docs.nexaai.com/)
If you're excited about the future of on-device AI, we'd really appreciate your support. A star on our GitHub repo goes a long way in helping us reach more developers!
Cheers,
Alex & Zack