تام مک‌لئود از آزمایشگاه i14labs از یه معماری جدید برای مدل‌های زبانی به اسم Sugar Glider رونمایی کرده و مدعی شده که با مصرف پردازش و حافظه بسیار کمتر، می‌تونه کارایی مدل‌های پیشرو رو ارائه بده

Frontier performance at a fraction of the compute. Today at @i14labs we’re launching Sugar Glider, our new family of LLM architectures! Join the waitlist for early access: http://i14.ai Sugar Glider delivers a new Pareto frontier: extreme memory and compute efficiency whilst maintaining frontier performance, even for large models (tested up to ~1T params). O(1) per token inference → ✅ Fixed size KV cache → ✅ Frontier long-context retrieval quality → ✅ 1 billion token long context retrieval → ☑️** (**100% -> single needle @ 1B-token context, 90.5% multikey 1 @ 1B-token context.) How did we do it? Turns out a few decades in the trenches of human memory and language model research is a pretty handy starting point to solve the “intelligence vs efficiency vs memory” conundrum. ... and just for something extra, we’ve been converting some of the most powerful and popular open-weight models to O(1) inference and fixed memory, with each conversion taking just hours :) Tune in via our waitlist for early model access http://i14.ai More results dropping soon!