Version 5.0 Modernizes DNN Engine, Adds LLM/VLM Support, and Enhances Core, Hardware Acceleration, and 3D Stack.

I built a local AI setup out of two old GPUs that sell for cheap, and it beats a single new card ...

Over the past year, local Large Language Models (LLMs) have made a massive leap forward. Today, a 7B parameter model running on a workstation can easily handle serious workloads—from IDE code ...

I performed a cross-test of 4 types of quantization (Q4 / Q5 / Q6 / Q8) on a popular local coder model that claims 67% on SWE-bench Verified, using my own 20-question benchmark. To cut to the chase, ...

A practical toolkit and step-by-step guide for quantizing ONNX models for Qualcomm® AI Runtime (QAIRT) and deploying them on Qualcomm NPUs. pip install ultralytics==8.4.58 onnx==1.21.0 ...

This article has been edited and created by AI. On Reddit's r/LocalLLaMA, discussions on optimizing local LLMs in real-world environments are intensifying. New insights backed by real-world ...

When your model is too big, too slow, or too expensive — and you need to decide what to actually do about it. You have trained or fine-tuned a model. It performs well. Then the production team runs ...

More parameters doesn't always mean more capabilities.

Abstract: Recent improvements in the accuracy of machine learning (ML) models in the language domain have propelled their use in a multitude of products and services, touching millions of lives daily.