DeepSeek’s conditional memory fixes silent LLM waste: GPU cycles lost to static lookups

Through systematic experiments DeepSeek found the optimal balance between computation and memory with 75% of sparse model ...
Amazon S3 on MSN

How to accelerate your LLMs by up to 29% with ASUS AI Cache Boost

Supercharge your large language model (LLM) workflows with AI Cache Boost. Exclusive to ASUS AMD 800 and 600 series ...
InfoWorld

Unlocking LLM superpowers: How PagedAttention helps the memory maze

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 ...
Semiconductor Engineering

Dynamic KV Cache Scheduling in Heterogeneous Memory Systems for LLM Inference (Rensselaer Polytechnic Institute, IBM)

A new technical paper titled “Accelerating LLM Inference via Dynamic KV Cache Placement in Heterogeneous Memory System” was published by researchers at Rensselaer Polytechnic Institute and IBM. “Large ...
VentureBeat

How attention offloading reduces the costs of LLM inference at scale

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Rearranging the computations and hardware used to serve large language ...
InfoWorld

Why LLM applications need better memory management

Generative AI applications don’t need bigger memory, but smarter forgetting. When building LLM apps, start by shaping working memory. You delete a dependency. ChatGPT acknowledges it. Five responses ...
ExtremeTech

Microsoft's New Compact 1-Bit LLM Needs Just 400MB of Memory

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Semiconductor Engineering

Pooling CPU Memory for LLM Inference With Lower Latency and Higher Throughput (UC Berkeley)

“The rapid growth of LLMs has revolutionized natural language processing and AI analysis, but their increasing size and memory demands present significant challenges. A common solution is to spill ...