What is GPU?GPU kya hai?

🧸 Think of it likeAise samjho

Like 1,000 students each solving one small sum at the same time, instead of one genius solving 1,000 sums one by one.Jaise 1,000 students ek saath ek ek chhota sawaal solve karein, ek genius ke 1,000 sawaal ek ek karke solve karne ki jagah.

📘 ExplainedVistaar se

Neural networks are mostly matrix multiplications, which GPUs do far faster than CPUs. GPU memory limits how big a model and batch can be. GPUs are expensive, so serving techniques like batching, quantisation and caching matter for cost.Neural networks zyaadatar matrix multiplications hain, jo GPUs CPUs se kahin tez karte hain. GPU memory tay karti hai model aur batch kitne bade ho sakte hain. GPUs mehenge hain, isliye batching, quantisation aur caching jaise serving tarike cost ke liye zaroori hain.

💡 Real exampleAsli example

Fine tuning a 7B model with QLoRA on one 24 GB GPU.Ek 24 GB GPU pe QLoRA se 7B model fine tune karna.

📖 Learn it step by stepStep by step seekho

• Serving LLMs efficiently: GPUs, batching and costLLMs ko efficiently serve karna: GPUs, batching aur cost

🔗 Related termsJude hue shabd

• QuantisationStoring model weights with fewer bits so models use less memory and run faster.Model weights ko kam bits mein rakhna taaki model kam memory le aur tez chale.
• Batch sizeHow many examples a model processes together in one training step.Ek training step mein model ek saath kitne examples process karta hai.
• InferenceUsing a trained model to make predictions on new data.Trained model se naye data pe predictions karna.