The single most practical lever for cheaper AI inference is quantization: running a model in lower numeric precision than it was trained in. In 2026 it's not exotic — it's the default, and the toolkit has matured into clear best practices.
The core trade
Models are typically trained in 16-bit precision. Quantization stores and computes the weights in fewer bits — 8-bit, 4-bit, sometimes lower — which shrinks memory use and speeds up computation. The risk is quality loss, and the art of good quantization is capturing most of the savings while giving up almost none of the accuracy.
Fewer bits, nearly the same answers. That's the whole game — and in 2026 the recipes are good enough that the tradeoff is often negligible.
The 2026 defaults
Two approaches dominate. FP8 has become the most stable general choice, especially on modern GPUs with native hardware support — it's a low-friction way to roughly halve precision with minimal quality loss. For weight quantization, AWQ (activation-aware weight quantization) is a common default for pushing to 4-bit, because it protects the weights that matter most for accuracy. Together they let large models run on far less memory.
Beyond weights
Quantization isn't only for model weights. Quantizing the KV cache — the memory that holds long-context state — to 8-bit cuts its footprint substantially, freeing GPU memory to serve more concurrent users. At long contexts, where the cache dominates memory, this is one of the biggest wins available.
The bottom line: quantization is the first thing to reach for when cutting inference cost. Start with FP8 for stability, explore 4-bit with AWQ where you need more savings, and quantize the KV cache for long-context workloads.
