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GPU cost-effectiveness for LLM serving depends on answer time, not just price

By PulseAugur Editorial · [1 sources] ·

A performance analysis comparing four GPUs (RTX 3090, RTX 5090, A100, and H100) for serving the Qwen2.5-7B-Instruct model using vLLM revealed that the cheapest GPU per token is not constant and depends on the required answer time. The NVIDIA H100 is the most cost-effective when very fast responses are needed, while the RTX 5090 becomes cheaper for slightly longer response times. The study also highlighted that different cloud provider pricing structures can significantly alter these cost comparisons, with a provided tool allowing users to input their own GPU prices for personalized analysis. AI

IMPACT Provides guidance for optimizing LLM inference costs based on latency requirements and GPU pricing.

RANK_REASON Benchmarking study comparing hardware performance for LLM inference. [lever_c_demoted from research: ic=1 ai=0.7]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

GPU cost-effectiveness for LLM serving depends on answer time, not just price

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Benchmarking study comparing hardware performance for LLM inference. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Qaiser Mehdi ·

    RTX 3090, RTX 5090, A100 and H100 on vLLM: the cheapest GPU flips with your answer time

    <p>I measured four GPUs serving the same 7B model on vLLM. Which one is cheapest per token has no single answer. It flips with how fast your answers must be.</p> <h2> The setup </h2> <p>Same model (Qwen2.5-7B-Instruct), same vLLM (0.27.1, default flags), same load on every card: …

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