14 gigabytes — that's how much a 7B model weighs in FP16. In 4-bit quantization — only 4. It's this difference you need to ca

AI Release 🚀 14 gigabytes — that's how much a 7B model weighs in FP16. In 4-bit quantization — only 4. It's this difference you need to calculate before buying a graphics card, rather than hoping for a reserve for the future. Free lessons will show how to estimate VRAM for a specific LLM: what quantization gives, how Ollama, llama.cpp, and vLLM differ, and why the same model can either fit into 8 gigabytes or require 24. This is not theory, but a working checklist — you can immediately check your configuration. Bottom line: first we calculate, then we buy. Otherwise, you risk overpaying for a card that will never work at full capacity. 🔗 Read on habr.com #LLM #GPU #localLLM #videocard #AI #neuralnetworks Habr Run LLM locally: what to calculate before buying a graphics card? Hello everyone, my name is Sergey Proshchaev, I am a Tech Lead and head of Java/Kotlin development at FinTech & E-commerce, and I teach development and architecture courses at OTUS.... 9 views 14:48

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