AI Tutorials
Optimizing 96GB VRAM for Local LLMs vs Paid APIs: A Technical Comparison
A deep dive into the technical and economic realities of running 4x RTX 3090s for local LLM inference versus utilizing high-performance LLM APIs.
Read more →
VRAM Optimization
Explore our entire collection of insights, tutorials, and industry news.
- AI Tutorials
Solving VRAM Constraints with TurboQuant for Efficient KV Cache Management
An in-depth technical analysis of Google's TurboQuant framework, exploring how PolarQuant and QJL residuals enable massive context windows through high-ratio KV cache quantization.Read more → - AI Tutorials
Gemma 4 and LLM Ops: Fine-Tuning, Local Inference, and VRAM Management
A comprehensive guide on managing Gemma 4 models, focusing on TRL v1.0 fine-tuning, llama.cpp tokenizer fixes, and strategies to overcome the significant KV cache VRAM demands on RTX hardware.Read more → - AI Tutorials
MoE vs Dense: Why a 35B Model Beats 27B on 8GB VRAM
A deep dive into why Mixture of Experts (MoE) architectures like Qwen3.5-35B-A3B outperform smaller dense models on consumer hardware with limited VRAM.Read more →