How Much VRAM You Actually Need to Run LLMs Locally in 2026

Running large language models locally on consumer hardware hinges on a single metric: VRAM, which must be large enough to hold the model's weights plus context overhead. The core formula estimates VRAM in gigabytes as the number of parameters multiplied by bits-per-weight, divided by eight. Most users rely on 4-bit quantization (Q4 GGUF format) to make models practical — shrinking a 7B model's memory footprint from roughly 14GB at FP16 down to about 4–6GB. Popular models like Llama 3 7B and Mistral 7B run comfortably on mainstream 8GB cards at Q4, while a 70B model still demands upwards of 44–48GB including context cache. The 2026 consumer flagship, NVIDIA's RTX 5090 with 32GB VRAM, handles models up to around 32B parameters at Q4 but falls short for 70B models without further compression or a reduced context window.