Google researchers have revealed that memory and interconnect are the primary bottlenecks for LLM inference, not compute power, as memory bandwidth lags 4.7x behind.

Detailed in a recently published technical paper, the Chinese startup’s Engram concept offloads static knowledge (simple ...

A new technical paper titled “Hardware-based Heterogeneous Memory Management for Large Language Model Inference” was published by researchers at KAIST and Stanford University. “A large language model ...

Researchers at the Tokyo-based startup Sakana AI have developed a new technique that enables language models to use memory more efficiently, helping enterprises cut the costs of building applications ...

Through systematic experiments DeepSeek found the optimal balance between computation and memory with 75% of sparse model ...

Seoul National University College of Engineering announced that a research team led by Professor Hyun Oh Song from the Department of Computer Science and Engineering has developed a new AI technology ...

In long conversations, chatbots generate large “conversation memories” (KV). KVzip selectively retains only the information useful for any future question, autonomously verifying and compressing its ...

Large language models (LLMs) like GPT and PaLM are transforming how we work and interact, powering everything from programming assistants to universal chatbots. But here’s the catch: running these ...

Think of continuous batching as the LLM world’s turbocharger — keeping GPUs busy nonstop and cranking out results up to 20x faster. I discussed how PagedAttention cracked the code on LLM memory chaos ...

Imagine having a conversation with someone who remembers every detail about your preferences, past discussions, and even the nuances of your personality. It feels natural, seamless, and, most ...