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JUPITER Advanced Research Vehicle for Inference Services

Project description

Making AI faster and ready for the real world

AI is moving from recognition to reasoning. Training powerful AI models to reason, respond, and adapt at scale is the next step in AI’s historical evolution. The main question is no longer how fast a model can learn, but how efficiently it can think. The EU-funded JARVIS project aims to bridge this gap. Specifically, it will work with JUPITER, which is Europe’s most powerful supercomputer and the fourth fastest in the world. The project will provide a high-performance platform specifically for the phase in which an AI model actually performs tasks. The objective is to build a next-generation AI that is not just faster and more energy efficient, but also ready for the demands of the real world.

Objective

JAIF will serve customers on the exascale supercomputer complex around JUPITER, scheduled to become operational in mid-2025. JUPITER is poised to be one of the strongest AI training machines in the world that allows, due to its superfast connectivity, full parallelism of training models on thousands of nodes. JAIF will help exploit JUPITER's outstanding capabilities and capacities, and leverage its transformative power for AI training.

In addition, the call offers the exciting opportunity to implement an experimental supercomputing platform for AI calculations. JAIF proposes to implement JARVIS, the JUPITER Advanced Research Vehicle for Inference Services. In JARVIS, a variety of experimental technological components of European origin will be integrated. JARVIS operates as a high-performance and highly scalable inference system that ideally complements JUPITER's AI training capabilities by achieving maximum throughput in terms of AI inference accessed through industry-standard cloud technologies.

JARVIS’ features are becoming very important for types of foundation models like largest language models (LLM) or reinforced LMMs like OpenAI’s o1 or DeepSeek-R1. The recent emergence of inference techniques, referred to as Inference Time-Scaling or as Test-Time Compute will require much larger scalable inference server capacities as the inference cycles become much longer due to the simulated reasoning iterations. In contrast to JUPITERs fully scalable connectivity, JARVIS will feature many islands of tightly connected nodes comprising between 4 and 16 large-memory nodes given current model sizes and those anticipated for the coming years. It is expected that this trend in AI algorithms will make the direct combination and tightest integration of training and inference systems on a modular hardware basis virtually essential.

Fields of science (EuroSciVoc)

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Programme(s)

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Funding Scheme

HORIZON-JU-RIA - HORIZON JU Research and Innovation Actions

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Call for proposal

(opens in new window) HORIZON-JU-EUROHPC-2025-AI-01-IBA

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Coordinator

FORSCHUNGSZENTRUM JULICH GMBH
Net EU contribution
€ 14 999 936,00
Address
WILHELM JOHNEN STRASSE
52428 JULICH
Germany
Region
Berlin Berlin Berlin
Activity type
Research Organisations
Links
Total cost
€ 29 999 872,00