A 120 billion parameter OpenAI model can now run on consumer hardware thanks to the Mixture of Experts (MoE) technique, which significantly reduces memory requirements and allows processing on CPUs while offloading key parts to modest GPUs.
The article shows how to check if a Linux CPU supports AES‑NI, Intel’s hardware‑accelerated AES instruction set. It explains what AES‑NI is, why it speeds up encryption, and then lists three easy methods: use cpuid and grep for “aes”, grep the /proc/cpuinfo file, or run lscpu and look for the “aes” flag. If none of these commands report AES‑NI, the CPU relies on slower software encryption, which is still secure. The first CPUs to expose this feature were Intel’s Westmere chips in 2010. In the CPUID specification the flag is simply called AES (bit 25 of ECX). The “NI” (New Instructions) part is just a marketing name for the feature set. There isn’t a distinct “aes_ni” bit in the CPUID leaf. So, when you run <tt>lscpu | grep -i aes or cat /proc/cpuinfo | grep aes</tt>, the presence of aes tells you that the CPU supports AES‑NI. There is no separate aes_ni flag because the kernel already uses the more concise aes.
LocalScore is an open benchmark to evaluate local AI task performance across various hardware configurations, measuring Prompt Processing speed, Token Generation speed, Time-to-First-Token (TTFT), and a combined LocalScore.
NVIDIA DGX Spark is a desktop-friendly AI supercomputer powered by the NVIDIA GB10 Grace Blackwell Superchip, delivering 1000 AI TOPS of performance with 128GB of memory. It is designed for prototyping, fine-tuning, and inference of large AI models.
6502.sh is a 6502 emulator and debugger written in busybox ash compliant shell script, featuring 32k RAM, 16k ROM, an interactive debugger, and STDIO directed to an ACIA compatible serial port.
A 6502 system emulated in a busybox ash shell script, featuring RAM, ROM, and an emulated serial port on STDIO, with built-in monitor and debugger.
This article explains how to accurately quantize a Large Language Model (LLM) and convert it to the GGUF format for efficient CPU inference. It covers using an importance matrix (imatrix) and K-Quantization method with Gemma 2 Instruct as an example, while highlighting its applicability to other models like Qwen2, Llama 3, and Phi-3.
