Reproducing NVIDIA's Inference Paper on 2xRTX 3090s Cuts TTFT by 2.2x at 32K Context
teortaxesTex · x · 2026-08-11
A developer successfully reproduced an NVIDIA inference optimization paper using two RTX 3090s on the stock vLLM framework.
The core mechanism is prefill decoupling: a small model handles the prefill for long contexts, passing its generated KV cache to a completely different, larger model for decoding.
The empirical results are significant:
- Speed: Time To First Token (TTFT) at a 32K context length dropped by 2.22x.
- Quality: Approximately 94% of the original output quality is retained.
- Engineering: No vLLM fork required; the entire setup runs via a single KV connector on stock vLLM.
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