One of the biggest challenges while running Large Language Models (LLMs) is answering a simple question:
👉 How much GPU memory (VRAM) do I actually need?
As DevOps, Platform, and SRE engineers, we're often asked to deploy models like Llama, Mistral, Qwen, or DeepSeek, but before choosing GPUs, we need to understand what actually occupies GPU memory during inference.
In today's session, we cover:
✅ Model weights and how quantization changes memory usage
✅ KV Cache and why it grows with context length
✅ Activation memory during inference
✅ Framework overhead and temporary buffers
✅ A practical formula to estimate VRAM requirements
✅ Real examples using popular open-source LLMs
By the end of this session, you'll be able to estimate GPU memory requirements before deploying an LLM, an essential skill for anyone working with AI infrastructure.
Looking forward to learning and growing together! 🚀
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