#LLM Benchmarking

A benchmark of Gemma 2 9B with FP8 quantization served on a single NVIDIA L4 GPU via vLLM reveals that FP8 increases time-to-first-token (TTFT) by up to 58% for long-context prompts, while reducing end-to-end latency for medium-length generation. This analysis exposes the hidden prefill tax of FP8 quantization on commodity hardware, helping engineers make informed trade-offs between latency, quality, and VRAM when deploying self-hosted LLMs. The unquantized model had a TTFT of 866.93ms for complex long-context prompts, while FP8 spiked to 1372.12ms; however, FP8 reduced average client total time from 12.29s to 11.53s for medium-length sequences and freed VRAM for larger batch sizes.