Abstract: Vector quantization (VQ) is a very effective way to save bandwidth and storage for speech coding and image coding. Traditional vector quantization methods can be divided into mainly seven ...

Abstract: This work introduces Semantically Masked Vector Quantized Generative Adversarial Network (SQ-GAN), a novel approach integrating semantically driven image coding and vector quantization to ...

self.register_buffer("weight", torch.zeros((out_features, in_features), dtype=torch.int8)) self.register_buffer("weight_scale", torch.zeros((out_features, 1), dtype ...

# The resulting model's .safetensors file should be 1.2GB, # whereas the original model's .safetensors file is 4.1GB. # See `./ex_llmcompressor_quantization.py` for how this can be # simplified using ...

Google Research unveiled TurboQuant, a novel quantization algorithm that compresses large language models’ Key-Value caches ...

As Large Language Models (LLMs) expand their context windows to process massive documents and intricate conversations, they encounter a brutal hardware reality known as the "Key-Value (KV) cache ...

AI has a growing memory problem. Google thinks it's found the answer, and it doesn't require more or better hardware. Originally detailed in an April 2025 paper, TurboQuant is an advanced compression ...

Google's TurboQuant reduces the KV cache of large language models to 3 bits. Accuracy is said to remain, speed to multiply. Google Research has published new technical details about its compression ...

The high cost of memory has sideswiped the technology industry, causing server vendors to admit their quotes are guesstimates and depressing sales of PCs and smartphones. Nobody is immune: Microsoft ...

If Google’s AI researchers had a sense of humor, they would have called TurboQuant, the new, ultra-efficient AI memory compression algorithm announced Tuesday, “Pied Piper” — or, at least that’s what ...