Why AI Models Run Faster on Some Chips: The KV Cache Effect

Описание: Learn more about KV Cache here → https://ibm.biz/~6LPSw4Y8N

The same AI model can run differently across chips. Kaoutar El Maghraoui explains how GPUs, TPUs, and AI accelerators store and move KV cache during LLM inference. Learn why memory architecture and data movement can shape AI performance.

00:00 – Understand Why LLMs Need a KV Cache
00:46 – See How KV Cache Reuses Keys and Values
01:39 – Balance KV Cache Capacity and Fast Memory
02:41 – Follow KV Cache Through GPU Memory
04:41 – Compare TPU Memory and Compiler Orchestration
06:44 – Explore Dataflow Accelerator Memory
08:34 – Compare How AI Accelerators Move KV Cache
09:05 – Connect KV Cache Movement to LLM Performance

Continue your education in the course Memory Systems in Dataflow Architectures on Coursera here → https://ibm.biz/~I7KAjOCAb

AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/~modQneKWV

AI was used in the creation of the transcript and metadata for this video.

#kvcache #llm #aimodels #aiaccelerator
---------------------------------------------------------------------------------------------------------
Find us on YouTube:
🔵 IBM Technology:    / @ibmtechnology  
🔵 IBM:    / @ibm  
🔵 IBM Developer:    / @ibmdeveloperadvocates