Comparison
Quantization vs Speculative Decoding
Quantization and Speculative Decoding are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Quantization
When inference memory or speed is the binding constraint and you can tolerate a small quality drop.
Llama-3-70B-INT4 running on a 48 GB GPU instead of needing 2 A100s.
When you would reach for Speculative Decoding
Speculative Decoding comes up when the question is fundamentally about inference.
Llama 3 70B accelerated by Llama 3 8B as draft.
Frequently asked
What is the difference between Quantization and Speculative Decoding?
Quantization: Quantization reduces model weights from 16- or 32-bit floats to lower-precision types (INT8, INT4) so the model needs less memory and runs faster, usually with minor quality loss. Speculative Decoding: Speculative decoding speeds up generation by having a small "draft" model propose several tokens, then verifying them in a single batched call to the big model.
When should I use Quantization vs Speculative Decoding?
When inference memory or speed is the binding constraint and you can tolerate a small quality drop. Speculative Decoding applies when you are focused on inference.
Are Quantization and Speculative Decoding the same thing?
No. Quantization is infrastructure; Speculative Decoding is inference. They are related but address different parts of the AI stack.