Accullm — Repack

In the race to build bigger, faster, and cheaper Large Language Models (LLMs), the industry has become obsessed with speed . We celebrate tokens-per-second, brag about billion-parameter counts, and marvel at 8-bit quantization that slashes memory usage.

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. accullm

When standard quantization rounds 3.14159 to 3 , it loses 0.14159 . Over billions of operations, this error accumulates like compound interest. AccuLLM uses stochastic rounding with error feedback —it tracks the rounding error from the last operation and injects it into the next one. The result? The average output matches the full-precision model, even if each individual step is wrong. The Shocking Use Case: Legal & Code Generation Why does this matter? Because for creative writing ("Write a poem about a cat"), 90% accuracy is fine. For retrieval-augmented generation (RAG) or code synthesis , 99.9% is the minimum. In the race to build bigger, faster, and