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

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

A new technical paper titled “Pushing the Envelope of LLM Inference on AI-PC and Intel GPUs” was published by researcher at Intel. “The advent of ultra-low-bit LLM models (1/1.58/2-bit), which match ...

Microsoft’s latest Phi4 LLM has 14 billion parameters that require about 11 GB of storage. Can you run it on a Raspberry Pi? Get serious. However, the Phi4-mini ...

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 ...

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 ...

Demand for AI solutions is rising—and with it, the need for edge AI is growing as well, emerging as a key focus in applied machine learning. The launch of LLM on NVIDIA Jetson has become a true ...

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 ...

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The AI world is experiencing a fundamental shift. After years of cloud-centric inference dominated by massive data center GPUs, we’re witnessing an accelerating migration of language models to edge ...