JUPITER Advanced Research Vehicle for Inference Services

Description du projet

Une IA plus rapide et prête pour monde réel

L’IA passe de la reconnaissance au raisonnement. La formation de modèles d’IA puissants capables de raisonner, de réagir et de s’adapter à grande échelle constitue la prochaine étape de l’évolution historique de l’IA. La question principale n’est plus de savoir à quelle vitesse un modèle peut apprendre, mais avec quelle efficacité il peut penser. Le projet JARVIS, financé par l’UE, entend répondre à cette question. Pour ce faire, il travaillera avec JUPITER, le superordinateur le plus puissant d’Europe et le quatrième plus rapide au monde. Le projet fournira une plateforme haute performance spécialement conçue pour la phase au cours de laquelle un modèle d’IA exécute effectivement des tâches. L’objectif est de construire une IA de nouvelle génération qui soit non seulement plus rapide et plus économe en énergie, mais aussi prête à répondre aux exigences du monde réel.

Objectif

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.