Local LLM Hardware guides & VRAM Optimization Guides
What hardware is required to run local LLMs?
Running local Large Language Models (LLMs) requires hardware optimized for high memory bandwidth and sufficient GPU VRAM. The absolute minimum requirement depends on model quantization, with 8GB VRAM supporting 7B parameter models, while 70B parameter models require multi-GPU configurations exceeding 48GB VRAM. Explore our technical deployment guides below.

Laguna S-2.1: 118B Model, 12GB Card. How CPU Offload Does It?
How Does a 118B Model Run on a 12GB GPU? It doesn’t: not the way...

Rent a GPU for AI: Run High-End Models Before You Buy
Can You Run High-End AI Without Buying a GPU? Yes — and it costs less...

Local AI API Server: Setup, Real Limits, and the Fix
How Do You Turn a Local LLM Into an API Server? Run it through Ollama...

Kimi K3 VRAM Requirements: What 2.8T Actually Takes
How Much VRAM Does Kimi K3 Need? The honest answer, as of July 16, 2026:...

llama.cpp Performance Flags That Matter on Low VRAM
Which llama.cpp Flags Actually Improve Performance? Three, when VRAM is your binding constraint (flag syntax...

Run 405B on 8GB VRAM? No One Has Published the Speed
Can You Really Run a 405B Model on 8 GB VRAM? Loading it: yes, demonstrably...

Ryzen AI Halo vs DGX Spark: 128GB AI Desktop Showdown
AMD Ryzen AI Halo vs NVIDIA DGX Spark: The 128GB AI Desktop Showdown Which is...

Best GPU for Local LLMs in 2026: The Complete Buying Guide
Best GPU for Local LLMs in 2026: The Complete Buying Guide What is the best...

Can My Gaming PC Run AI? The 2026 GPU Tier Guide
Can My Gaming PC Run Local AI? The 2026 GPU Tier Guide Can a gaming...