Will Your LLM Fit in VRAM? My Free LLM Memory Calculator

Published 27/09/2026

Description

Will your next local LLM actually fit in GPU memory—or will a larger context window force it to offload and slow down? In this video, I introduce my LLM Memory Calculator and show how to estimate memory use before downloading a model. Contents: 0:00 - Introduction 0:20 - Description 1:15 - Demo 8:41 - Conclusion I walk through model parameters, weight quantization, context length, KV-cache quantization, batch sizes, parallel requests, and runtime selection. You can enter model details manually or paste a Hugging Face model URL to prefill the details the calculator can find, then adjust the values yourself. Using Qwen 3.8 27B on my RTX 5090 as an example, I show why a quantized model that appears to fit comfortably at a short context can become tight at a much longer one—and how changing KV-cache settings or parallelism affects the estimate. I also demonstrate the difference between dedicated GPU memory and unified memory, including why other applications can reduce what is actually available. The calculator is a planning tool, not a guarantee: real usage depends on your runtime, operating system, and other processes. If you spot a model or setting it estimates incorrectly, let me know. #LocalLLM #GPUMemory #LLMMemoryCalculator Calculator https://llmmemorycalculator.samayas.eu/ Follow me on X: https://l.samayas.eu/jo3eaYEf3E?P=YT&V=168 Buy me a Coffee : https://l.samayas.eu/jX6xL7EWhd?P=YT&V=168 Studio Equipment Camera Sony Alpha ZV-E10 II: https://l.samayas.eu/k7y95TREl0?P=YT&V=168 Secondary Camera Obsbot Tail Air: https://l.samayas.eu/iFQu9WfmCw?P=YT&V=168 Elgato Prompter: https://l.samayas.eu/fHVrqqXtSc?P=YT&V=168 Microphone Rode Wireless GO II: https://l.samayas.eu/afBINjiBXN?P=YT&V=168