In case you’re like me, you in all probability get excited concerning the newest and best open-source LLMs — from fashions like Llama 3 to the extra compact Phi-3 Mini. However earlier than you bounce into deploying your language mannequin, there’s one essential issue you want to plan for: GPU reminiscence. Misjudge this, and your shiny new net app would possibly choke, run sluggishly, or rack up hefty cloud payments. To make issues simpler, I clarify to you what’s quantization, and I’ve ready for you a GPU Reminiscence Planning Cheat Sheet in 2024— a useful abstract of the most recent open-source LLMs in the marketplace and what you want to know earlier than deployment.
When deploying LLMs, guessing how a lot GPU reminiscence you want is dangerous. Too little, and your mannequin crashes. An excessive amount of, and also you’re burning cash for no purpose.
Understanding these reminiscence necessities upfront is like realizing how a lot baggage you possibly can slot in your automotive earlier than a highway journey — it saves complications and retains issues environment friendly.