Google researchers have published a new quantization technique called TurboQuant that compresses the key-value (KV) cache in ...

Google introduces TurboQuant, a compression method that reduces memory usage and increases speed ...

It turns out the rapid growth of AI has a massive downside: namely, spiraling power consumption, strained infrastructure and runaway environmental damage. It’s clear the status quo won’t cut it ...

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

Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...

Huawei, a major Chinese technology company, has announced Sinkhorn-Normalized Quantization (SINQ), a quantization technique that enables large-scale language models (LLMs) to run on consumer-grade ...

SEOUL, South Korea, March 5, 2026 /PRNewswire/ -- Nota AI, an AI optimization technology company behind the Nota AI brand, announced that it has developed a next-generation quantization technology ...

The reason why large language models are called ‘large’ is not because of how smart they are, but as a factor of their sheer size in bytes. At billions of parameters at four bytes each, they pose a ...

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

One-bit large language models (LLMs) have emerged as a promising approach to making generative AI more accessible and affordable. By representing model weights with a very limited number of bits, ...