Comparison
Quantization vs vLLM
Quantization and vLLM 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 vLLM
vLLM comes up when the question is fundamentally about infrastructure.
Serving Llama 3 70B at high QPS on 4 H100s with vLLM.
Frequently asked
What is the difference between Quantization and vLLM?
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. vLLM: vLLM is an open-source high-throughput LLM serving engine. Its PagedAttention KV cache manager is the reason it dramatically outperforms naive serving setups.
When should I use Quantization vs vLLM?
When inference memory or speed is the binding constraint and you can tolerate a small quality drop. vLLM applies when you are focused on infrastructure.
Are Quantization and vLLM the same thing?
No. Quantization is infrastructure; vLLM is infrastructure. They are related but address different parts of the AI stack.