Ollama Runs 32B Local AI Models on a $599 Mac via Quantization for Free

Learn how to run a 32B local LLM on a $599 Mac Mini using Ollama. This setup reduces cloud AI costs while maintaining strong ...

Changing AI math could reduce the hardware burden, researchers show

Sophisticated AI models tend to require a lot of memory and take up a lot of storage space. One of the ways to reduce that ...
EE World Online

Why small language models win at the Edge

By Pietro Antonio Ciclese, Senior Technical Marketing Engineer, Ambarella The workloads that generate the most commercial ...
IEEE

Approximate DCT and Quantization Techniques for Energy-Constrained Image Sensors

Abstract: Recent expansions in multimedia devices for many applications, such as surveillance, self-driving cars, and healthcare, gather enormous amounts of real-time images for processing and ...
IEEE

TensorRT Implementations of Model Quantization on Edge SoC

Abstract: Deep neural networks have shown remarkable capabilities in computer vision applications. However, their complex architectures can pose challenges for efficient real-time deployment on edge ...
note

[For CUDA 16GB] llama.cpp b9542 adds Gemma4/Qwen3 support, Qwen3-8B at Q4_K_M hits 432 prefill token/74.7 tok/s (CUDA 13.3 environment)

llama.cpp has released b9542, including pre-built binaries for CUDA 13.3 and 12.4. It includes MTP specification fixes and Gemma4 support. vLLM, alongside its 0.22.1 patch release, implemented CUDA ...