Hackaday

TurboQuant: Reducing LLM Memory Usage With Vector Quantization

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the ...
Communications of the ACMOpinion

The Golden Rule of Big Memory: Persistence Is Not Harmful

Large-scale applications, such as generative AI, recommendation systems, big data, and HPC systems, require large-capacity ...
Tech Xplore

CacheMind turns chip tuning into a conversation, exposing hidden cache failures and lifting processor performance

Researchers at North Carolina State University have developed a new AI-assisted tool that helps computer architects boost ...

What Google's TurboQuant can and can't do for AI's spiraling cost

What Google's TurboQuant can and can't do for AI's spiraling cost ...

Crisis of Memory: Citi Just Cut Its Micron Stock Price Target by 16%

Memory prices are softening after Google figured out a way to make memory usage more efficient. Is this the death knell for ...

Is increasing VRAM finally worth it? I ran the numbers on my Windows 11 PC

Is increasing VRAM finally worth it? I ran the numbers on my Windows 11 PC ...

Google's TurboQuant saves memory, but won't save us from DRAM-pricing hell

This is really where TurboQuant's innovations lie. Google claims that it can achieve quality similar to BF16 using just 3.5 ...

We Tested Every Ryzen 5 and 7 X3D CPU: From 5800X3D to 9800X3D

Nine X3D CPUs, two platforms, and 14 games tested. We compare every Ryzen 5 and 7 X3D processor to find out how much performance has improved ...
Hosted on MSN

Google says TurboQuant cuts LLM KV-cache memory use 6x, boosts speed

Google researchers have published a new quantization technique called TurboQuant that compresses the key-value (KV) cache in large language models to 3.5 bits per channel, cutting memory consumption ...