Accullm [verified] ›

And for the next generation of AI agents handling your money, health, and code—almost isn't good enough.

Ask a standard quantized LLM to calculate 523 * 19 or to cite the 7th word of the 4th sentence of a provided contract. It often fails—not because it isn’t smart, but because it was sacrificed on the altar of efficiency. This is where enters the arena. The Core Problem: The Leaky Bucket of Precision Most LLMs run on floating-point math (FP16 or BF16). To make them faster, engineers use quantization (INT8, INT4, or even INT2). This is like listening to an MP3 instead of a vinyl record—99% of the time it sounds fine, but that 1%—the high-frequency data, the exact integer logic, the specific retrieval—becomes "lossy." accullm

When your chatbot hallucinates a date, that's amusing. When your quantized SQL generator drops a foreign key constraint, that's a catastrophe. AccuLLM is the quiet, nerdy hero ensuring that as we make AI smaller and faster, we don't make it stupider. And for the next generation of AI agents