Google Research's TurboQuant algorithm achieves 8x memory speedup and 50% cost reduction for LLM inference. The open research release compresses KV cache by 6x with zero accuracy loss, already being ported to…
Google Research releases TurboQuant, a KV cache compression algorithm that reduces AI memory usage by 6x with zero accuracy loss, potentially cutting cloud AI costs by 50% or more.
Google Research releases TurboQuant algorithm enabling 6x memory reduction and 8x speed boost for AI models. Enterprise costs could drop by 50 percent or more.
Google Research's TurboQuant algorithm achieves 6x KV cache compression and 8x speedup, cutting AI inference costs by 50% or more through mathematical breakthroughs in quantization.
Google Research unveils TurboQuant, a groundbreaking compression algorithm that reduces AI memory usage by 6x while maintaining full model accuracy, potentially cutting AI deployment costs by 50% or more.
Google Research unveils TurboQuant, a groundbreaking compression algorithm that reduces AI memory usage by 6x while maintaining full model accuracy, potentially cutting AI deployment costs by 50% or more.
Google TurboQuant algorithm achieves 6x KV cache compression with zero accuracy loss 鈥?and runs up to 8x faster on H100 GPUs.
xMemory introduces a four-level semantic hierarchy that cuts AI agent token usage nearly in half, promising to revolutionize long conversation and multi-session AI task handling.
Google Research releases TurboQuant algorithm that compresses AI memory usage by 6x with zero accuracy loss, potentially cutting enterprise costs by 50% or more.
Google Research's new TurboQuant algorithm achieves 6x KV cache compression with zero accuracy loss, potentially cutting enterprise AI inference costs by 50% or more. The technology is now freely available.
