TurboQuant cuts LLM memory 6x without accuracy loss

TurboQuant signals the end of the memory-bound era for LLM inference, shifting the bottleneck back to compute and potentially tanking high-bandwidth memory premiums.

• –Achieves 3.5 bits per channel with "absolute quality neutrality," effectively solving the KV cache bloat problem for long-context windows.
• –The "data-oblivious" nature means it works out-of-the-box for models like Llama-3.1, Gemma, and Mistral without needing specific datasets for calibration.
• –Elimination of per-block metadata overhead is a massive engineering win, simplifying kernel implementation while maximizing throughput.
• –Consumer hardware gains the most: 70B models that previously required enterprise VRAM could soon run on high-end consumer GPUs with massive context.
• –Beyond LLMs, this tech accelerates vector search and searchable AI indexes, making it a foundational infrastructure improvement.