The Next Web

Google’s new compression algorithm cut memory stocks within hours of publication

Google's TurboQuant algorithm compresses LLM key-value caches to 3 bits with no accuracy loss. Memory stocks fell within ...
Decrypt

Google Shrinks AI Memory With No Accuracy Loss—But There's a Catch

The technique reduces the memory required to run large language models as context windows grow, a key constraint on AI ...

Google’s TurboQuant AI-compression algorithm can reduce LLM memory usage by 6x

Google Research recently revealed TurboQuant, a compression algorithm that reduces the memory footprint of large language ...

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 ...
WinBuzzer

Google’s TurboQuant Algorithm Slashes LLM Memory Use by 6x

Google has published TurboQuant, a KV cache compression algorithm that cuts LLM memory usage by 6x with zero accuracy loss, ...

Google's TurboQuant compression tech cuts LLM memory use by 6x with no accuracy loss

The biggest memory burden for LLMs is the key-value cache, which stores conversational context as users interact with AI ...
Investing.com India

MU, WDC, SNDK fall: Why Google’s TurboQuant is rattling memory stocks

Memory stocks fell Wednesday despite broader technology sector strength, with shares dropping after Google unveiled ...

What is Google's new AI algorithm that has sent stocks of Samsung and other biggest memory makers plummeting

Google's new TurboQuant algorithm drastically cuts AI model memory needs, impacting memory chip stocks like SK Hynix and ...
Electronic Design

Algorithm Delivers Lossless Compression To ADC Samples

Compression algorithms for speech, audio, still images, and video are quite complicated and, more importantly, nearly always lossy. Thus, samples often change dramatically once they’re decompressed.
Electronic Design

Try Alternative Bit Packers For Compression

Compression reduces bandwidth and storage requirements by removing redundancy and irrelevancy. Redundancy occurs when data is sent when it’s not needed. Irrelevancy frequently occurs in audio and ...