SDxCentral

DeepSeek looks to offload simple LLM tasks to save billions of parameters

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

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 ...
Semiconductor Engineering

Optimizing LLM Training Under GPU Memory Constraints (Argonne, RIT)

A new technical paper titled “MLP-Offload: Multi-Level, Multi-Path Offloading for LLM Pre-training to Break the GPU Memory Wall” was published by researchers at Argonne National Laboratory and ...
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 ...
Investing

Alibaba Cloud optimizes GPU usage for LLM inferencing, cuts needs by 82%

LLM inferencing typically involves numerous burst requests, which creates challenges for efficient GPU usage. Alibaba Cloud improved efficiency by implementing a model that processes work based on ...
Geeky Gadgets

GPU-Accelerated LLMs : Deploying A GPU-Powered AI Model on Cloud Run

What if you could deploy a innovative language model capable of real-time responses, all while keeping costs low and scalability high? The rise of GPU-powered large language models (LLMs) has ...