Quantization

Most AI efficiency launches are either smaller weights, benchmark theater, or a kernel trick dressed up as a new paradigm. TurboQuant is more interesting than that. On March 24, 2026, Google Research published TurboQuant as a practical compression stack for KV caches and vector search. The public claim was blunt: at least 6x KV-cache reduction, up to 8x attention-logit speedup on H100, and no training or fine-tuning required. Underneath the marketing, the real contribution is cleaner and more important: Google found a way to make extreme low-bit vector quantization behave like a systems primitive instead of a fragile research demo. S1 ...

Qwen3-30B-A3B represents a paradigm shift in large language model efficiency, achieving flagship-level performance with only 3.3 billion active parameters from a 30.5 billion total parameter pool. This Mixture-of-Experts (MoE) model, released by Alibaba’s Qwen team, demonstrates that intelligent parameter activation can outperform brute-force scaling, scoring 91.0 on ArenaHard while using 10x fewer active parameters than comparable dense models. The model’s hybrid thinking architecture enables controllable reasoning depth, supporting both rapid responses and deep analytical tasks through dynamic computational allocation. ...