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AI Services Consume 17.8 GB VRAM on 24 GB GPU in Multi-Tenant Test

By PulseAugur Editorial · [1 sources] ·

A recent experiment detailed how much VRAM is actually consumed by running multiple AI services concurrently on a single GPU. The test involved an NVIDIA RTX 3090 with 24 GB of VRAM, hosting a Whisper ASR webservice, a text-to-speech model, and an embedding model for a RAG pipeline. The combined usage averaged 17.8 GB, with peaks reaching 23.4 GB, demonstrating that the actual VRAM requirement for a service is significantly higher than just the model weights due to factors like working sets, framework overhead, and data buffers. AI

IMPACT Provides practical insights for users sizing GPUs for local AI deployments, highlighting that actual VRAM needs exceed model weight estimates.

RANK_REASON The item details a specific technical experiment and its findings regarding VRAM consumption for AI services. [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 →

AI Services Consume 17.8 GB VRAM on 24 GB GPU in Multi-Tenant Test

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The item details a specific technical experiment and its findings regarding VRAM consumption for AI services. [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) · Arsen Apostolov ·

    I Put Three AI Services on One 24 GB GPU. It Used 17.8. Here's What Fits.

    <h1> I Put Three AI Services on One 24 GB GPU. It Used 17.8. Here's What Fits. </h1> <p><strong>The Local LLM Lab — Part 9</strong></p> <blockquote> <p><em>One controlled experiment. One number. One verdict.</em></p> </blockquote> <p>The "how much VRAM do I need" advice on the in…

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