Embeddings Are Overpriced? A Frozen 16 MB Vocabulary Lets a 46 MB Ternary Gemma-Style Model Run 5x Faster on CPU
A new paper replaces Gemma 3 270M's 168M-parameter embedding table with frozen 512-bit codes. Trained on the same 2.05B tokens, it ties or beats the learned table on LAMBADA, HellaSwag and PIQA, and a ternary version is a 46 MB file running 615 to 740 tokens per second on a laptop CPU.









