If you’re planning to run large language models locally, the first question you need to answer isn’t about CPU speed or storage—it’s about VRAM. Video memory determines what models you can run, at what quality level, and how responsive they’ll be. Get this wrong and you’ll either overspend on hardware you don’t need or build … Read more
Running large language models locally has become increasingly practical in 2026, but choosing the right GPU can make or break your experience. If you’re weighing the RTX 3060, 4060, or 4090 for local LLM inference, you’re asking the right question—but the answer isn’t straightforward. VRAM capacity, not just raw compute power, determines what models you … Read more
Running large language models (LLMs) on your own hardware offers privacy, control, and cost savings compared to cloud-based solutions. However, the primary bottleneck most users face is VRAM (Video Random Access Memory) limitations. Modern LLMs can require anywhere from 4GB to 80GB of VRAM, making them inaccessible to users with consumer-grade GPUs. Fortunately, several proven … Read more
