Semiconductor Engineering

Efficient LLM Inference With Limited Memory (Apple)

A technical paper titled “LLM in a flash: Efficient Large Language Model Inference with Limited Memory” was published by researchers at Apple. “Large language models (LLMs) are central to modern ...
Semiconductor Engineering

LLM Inference On CPUs (Intel)

“Large language models (LLMs) have demonstrated remarkable performance and tremendous potential across a wide range of tasks. However, deploying these models has been challenging due to the ...
Business Wire

Enfabrica Unveils Industry’s First Ethernet-Based AI Memory Fabric System for Efficient Superscaling of LLM Inference

MOUNTAIN VIEW, Calif.--(BUSINESS WIRE)--Enfabrica Corporation, an industry leader in high-performance networking silicon for artificial intelligence (AI) and accelerated computing, today announced the ...
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 ...
TweakTown

Dell PowerEdge XE9712: NVIDIA GB200 NVL72-based AI GPU cluster for LLM training, inference

Dell has just unleashed its new PowerEdge XE9712 with NVIDIA GB200 NVL72 AI servers, with 30x faster real-time LLM performance over the H100 AI GPU. Dell Technologies' new AI Factory with NVIDIA sees ...
Hosted on MSN

Level up your LLM speed and efficiency

Deploying large language models can be slow and costly, but smart optimization changes that. From GPU memory tricks to hybrid CUDA graph execution, new methods are slashing latency and boosting ...
InfoWorld

Google targets AI inference bottlenecks with TurboQuant

Google says its new TurboQuant method could improve how efficiently AI models run by compressing the key-value cache used in LLM inference and supporting more efficient vector search. In tests on ...
Forbes

The Inference Economy: How Sparse Computing And Model Optimization Are Reshaping Enterprise AI Deployment

The AI industry stands at an inflection point. While the previous era pursued larger models—GPT-3's 175 billion parameters to PaLM's 540 billion—focus has shifted toward efficiency and economic ...