Why Unified Memory Lets Mini PCs Run Large AI Models That GPUs Can't Fit
One idea explains the whole mini PC category: unified memory lets a $2,000 box hold a 70B model no consumer GPU can fit, then decode it slowly. The roofline math, the prompt-processing catch, the NPU…
Read the full articleYou might also wanna read
Researchers Serve 229B-Parameter MoE Model Across Five Consumer GPUs Over Public Internet
We serve MiniMax-M2.5, a 229B-parameter mixture-of-experts model, split across five consumer RTX 5090s in five European countries. The stage
Linux vs Windows VRAM Usage for Local AI
Linux saves ~800MB VRAM over Windows for local AI models. Benchmarks across 100+ tests reveal why memory savings matter more than raw speed
Understanding VRAM Requirements for Local AI Coding
Learn how GPU memory determines which AI models you can run locally, and how to select the right model for your hardware constraints.
How We Optimised a 229 Billion Parameter AI Model on a Desktop Computer: A 12-Phase Journey
We deployed MiniMax M2.7 (229B params) on a single NVIDIA DGX Spark and spent a day optimising it. Thread tuning added 12% speed, --no-mmap
AI-Driven Approach for Portable GPU Kernels in High-Performance Computing
High-Performance Computing (HPC) applications increasingly depend on GPUs, yet developing optimized kernels across evolving GPU architecture
GIGABYTE Demonstrates AI TOP ATOM Four-Node Clustering for Local AI Computing
GIGABYTE, the world's leading computer brand, demonstrates how AI TOP ATOM four-node clustering scales local AI computing for increasingly c

Comments
Sign in to join the conversation.
No comments yet. Be the first.