Comparison
Speculative Decoding vs vLLM
Speculative Decoding and vLLM are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
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.
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 Speculative Decoding and vLLM?
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. 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 Speculative Decoding vs vLLM?
Speculative Decoding is the right concept when you are focused on inference. vLLM applies when you are focused on infrastructure.
Are Speculative Decoding and vLLM the same thing?
No. Speculative Decoding is inference; vLLM is infrastructure. They are related but address different parts of the AI stack.