๐ Day 3 of 100 Days of GenAI for DevOps is LIVE!
One of the biggest challenges while running Large Language Models (LLMs) is answering a simple question: ๐ How much GPU memory (VRAM) do I actually need? As DevOps, Platform, and SRE engineers, we're often asked to dep
One of the biggest challenges while running Large Language Models (LLMs) is answering a simple question:
๐ 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:
โ Model weights and how quantization changes memory usage
โ KV Cache and why it grows with context length
โ Activation memory during inference
โ Framework overhead and temporary buffers
โ A practical formula to estimate VRAM requirements
โ 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! ๐
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Day 3 (English): https://lnkd.in/g3xh5Tgi
Day 3 (Hindi):
โก๏ธ If you're looking to try an AI agent built specifically for DevOps, check out **DevOps Open Agent:ย
Originally published by Dev.to AI. Aggregated on AIWithGhost for educational purposes โ full credit and traffic to the original publisher.