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Qwen2.5-7B-Instruct performance benchmarked on NVIDIA GPUs

By PulseAugur Editorial · [1 sources] ·

A performance analysis of the Qwen2.5-7B-Instruct model was conducted across three NVIDIA GPUs: RTX A6000, L40S, and H100, using the vLLM framework. The study focused on tokens per second and the cost per million tokens generated. Results indicated that while the L40S offered similar performance to the RTX A6000 in single-stream decoding, the A6000 was more cost-effective. The H100 excelled in latency, providing significantly higher throughput and faster time-to-first-token at high concurrency, making it suitable for chat applications despite its higher cost. AI

IMPACT Provides cost and performance data for self-hosting LLMs, aiding infrastructure decisions.

RANK_REASON Benchmarking study of model performance on hardware. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

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

Qwen2.5-7B-Instruct performance benchmarked on NVIDIA GPUs

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32 / 100
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Benchmarking study of model performance on hardware. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

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

    vLLM on three GPUs: Qwen2.5-7B tokens per second and cost per 1M tokens on RTX A6000, L40S and H100

    <p>I wanted one number before picking a GPU for a small self-hosted model: what does a million generated tokens cost on each card, once the server is actually busy? Spec sheets don't answer that, so I rented three single-GPU machines on Runcrate this morning (2026-10-26), ran the…

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