AI BriefingOptimizing Gemma 4 Serving with vLLM on Amazon EKS [Part 2: Throughput and Latency SLOs on a Single GPU]
Key point
This article introduces vLLM optimization strategies to maximize Gemma 4's SLO-compliant throughput through NVFP4 precision and the separation of interactive and batch pools.
Details
Precision Comparison and SLO Compliance
NVFP4 demonstrates up to 1.86x higher throughput compared to BF16, and a 6.3x performance improvement under SLO constraints. While BF16 and FP8 already exceed SLOs at concurrency 1, NVFP4 satisfies TTFT p99 500ms and ITL p99 50ms up to concurrency 4. At concurrency 32, compute bottlenecks emerge, resulting in a 1.20x difference compared to FP8 due to the utilization of FP4 tensor cores.
Speculative Decoding and Pool Separation
Speculative Decoding (MTP n=4) increases throughput by up to 2.13x but degrades ITL p99 by more than 4x, violating interactive SLOs. Therefore, separating interactive and batch traffic into distinct pools is essential. The interactive pool disables MTP, while the batch pool applies MTP with lower priority.
Prefix Caching and Tuning
Prefix Caching increases throughput by 40.5% and reduces TTFT by 2.1x when prompt sharing rates are high. Since throughput per GPU can vary by up to 10x depending on hit rates, optimizing prompt structure is crucial. In text-only pools, releasing image encoder reservations can increase KV cache capacity by 12.6%.
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