How are engineers making AI models faster and cheaper to run?

The answer

AI inference optimization has become a critical field as deployment costs soar. Quantization reduces model weights from 32-bit to 8-bit, 4-bit, or even 2-bit precision, dramatically cutting memory usage and speeding computation. NVIDIA released FP4-quantized versions of DeepSeek-R1 and Llama-3.3-70B on Hugging Face with minimal accuracy loss. Speculative decoding uses a small 'draft' model to propose multiple tokens ahead, then verifies them in parallel with the full model. Google's EAGLE-3 adds a tiny draft head - just 2-5% of the target model's size - achieving 2-3x decoding speedup, making powerful AI accessible at a fraction of the cost.