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Untangling GPU Power Consumption: Job-Level Inference in Cloud Shared Settings

Pierre Jacquet, Manuel Agustí, Eddy Caron, Camille Coti, Marcos Dias de Assunção, Laurent Lefèvre, Anne‐Cécile Orgerie
April 27, 2026
Published Date

Research Abstract & Technology Focus

International audience
Computer science Cloud computing Inference Power (physics) Key (lock) Computer graphics (images)
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openalex.org › research concept
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Untangling GPU Power Consumption: Job-Level Inference in Cloud Shared Settings

International audience
github.com › AI insight
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turbo3/turbo4 cache produces garbled output on NVIDIA Blackwell GPU (RTX 5070 Laptop, compute capability 12.0)

This issue exposes a critical compatibility gap for TurboQuant's CUDA kernels on NVIDIA's new Blackwell architecture (sm_120). The failure to produce coherent output with `turbo3`/`turbo4` cache ty...
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Efficient-tuning

Optimization for local LLM inference is shifting focus to GPU memory clock performance, with NVIDIA RTX GPUs accelerating local AI deployment. This highlights a critical technical trend in efficien...
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Pytorch

The AI hardware landscape is intensifying with new entrants like Korean startup Rebellions and Meta's custom MTIA chips directly challenging Nvidia's dominance, focusing on efficient AI inference w...
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GPT not detected (Windows 11- RTX3060 12GB)

This issue reveals a fundamental failure in OBLITERATUS's ability to detect and utilize available GPU hardware (RTX 3060 12GB) on a Windows 11 system. The system defaults to 'CPU mode' despite sign...

Frequently Asked Questions (FAQ)

Curated market intelligence mapped to this research.

What is the core focus of the research titled 'Untangling GPU Power Consumption: Job-Level Inference in Cloud Shared Settings'?

This literature focuses on: International audience

Are there open-source GitHub repositories related to Untangling GPU Power Consumption: Job-Level Inference in Cloud Shared Settings?

Yes, open-source projects like NVIDIA/NemoClaw (Run OpenClaw more securely inside NVIDIA OpenShell with managed inference) are actively building upon these concepts.

Which startups are commercializing the technology behind Untangling GPU Power Consumption: Job-Level Inference in Cloud Shared Settings?

Products like General Compute are bringing this to market. Their focus is: AI models that run on an inference cloud optimized for speed.

What other academic literature is closely related to 'Untangling GPU Power Consumption: Job-Level Inference in Cloud Shared Settings'?

Yes, highly correlated activity was mapped. An entry titled 'Untangling GPU Power Consumption: Job-Level Inference in Cloud Shared Settings' discusses this: International audience

Are there commercial applications of 'Untangling GPU Power Consumption: Job-Level Inference in Cloud Shared Settings' in market news publications?

Yes, highly correlated activity was mapped. An entry titled 'Efficient-tuning' discusses this: Optimization for local LLM inference is shifting focus to GPU memory clock performance, with NVIDIA RTX GPUs accelerating local AI deployment. This...

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