Lumai Launches the World’s First Optical Computing System for Real-Time, Billion-Parameter LLM Inference

Lumai, the optical compute company addressing scalable AI, today announced its Lumai Iris inference server – the world’s first optical computing system to successfully run billion-parameter large ...
TMCnet

Skymizer Taiwan Inc. Unveils Breakthrough Architecture Enabling Ultra-Large LLM Inference on a Single Card

Founded in 2013, Skymizer is an AI inference company. Its flagship HyperThought platform pairs a compiler-driven software stack with transformer-optimized hardware to deliver high-efficiency inference ...
telecom.economictimes.indiatimes

Turiyam AI deploys inference engine on C-DAC’s indigenous server architecture

NEW DELHI: Homegrown Turiyam AI said on Thursday it has deployed its inference engine on an indigenous server architecture at the Centre for Development of Advanced Computing (C-DAC) in Pune. The ...
VentureBeat

Pipeshift cuts GPU usage for AI inferences 75% with modular interface engine

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now DeepSeek’s release of R1 this week was a ...
SiliconANGLE

New memory architecture targets AI inference bottlenecks

Lightbits Labs Ltd. today is introducing a new architecture aimed at addressing one of the most stubborn bottlenecks in large-scale artificial intelligence inference: the growing mismatch between the ...
New Atlas

Next-level AI engine comes top in LLM speed showdown

Responses to AI chat prompts not snappy enough? California-based generative AI company Groq has a super quick solution in its LPU Inference Engine, which has recently outperformed all contenders in ...
Forbes

Distributed Intelligence Is Here—And Reshaping Device Architecture

Iri Trashanski, Chief Strategy Officer at Ceva, is shaping the future of the Smart Edge with extensive experience across tech sectors. AI inference is happening across a network of local ...
VentureBeat

The team behind continuous batching says your idle GPUs should be running inference, not sitting dark

Every GPU cluster has dead time. Training jobs finish, workloads shift and hardware sits dark while power and cooling costs keep running. For neocloud operators, those empty cycles are lost margin.