Google's new TurboQuant algorithm speeds up AI memory 8x, cutting costs by 50% or more

Within 24 hours of the release, community members began porting the algorithm to popular local AI libraries like MLX for Apple Silicon and llama.cpp.
Morning Overview on MSN

Google’s TurboQuant algorithm slashes the memory bottleneck that limits how many AI models can run at once

Running a large language model is expensive, and a surprising amount of that cost comes down to memory, not computation.
Hosted on MSN

AI market pivots to inference as CPUs and memory gains reshape race

Inference takes center: The industry focus is shifting from training to inference, where CPUs and orchestration tools are increasingly critical for AI performance. Chip leaders shift: AMD and Intel ...
The Verge

Meta’s new AI chips run faster than before

The next-generation MTIA chip could be expanded to train generative AI models. The next-generation MTIA chip could be expanded to train generative AI models. Meta promises the next generation of its ...
Electronic Design

Bring Deep-Learning Inference to Embedded Applications

Deep learning, probably the most advanced and challenging foundation of artificial intelligence (AI), is having a significant impact and influence on many applications, enabling products to behave ...
InfoQ

Java Feature Spotlight: Local Variable Type Inference

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Dany Lepage discusses the architectural ...
Business Insider

Nvidia might actually lose in this key part of the AI chip business

Inference made up 40% of Nvidia's $26.3 billion Q2 data center revenue. Inference computing demand will increase as AI matures. Companies like Groq and Cerebras are launching inference chips to ...