Deneb: TU Dresden’s New Energy‑Efficient AI Supercomputer Powered by NVIDIA Grace Blackwell

Breaking: TU Dresden Reveals Deneb, a New AI-Optimized Supercomputer With Q4 2026 Go-Live

A major advancement in European high-performance computing rolls forward as TU Dresden’s Center for Details services and High Performance Computing introduces Deneb. The system is slated to become operational in the fourth quarter of 2026, marking a significant boost for AI research at the university.

Designed for demanding artificial intelligence workloads, Deneb centers on NVIDIA’s Grace Blackwell architecture. The system blends two Grace Blackwell accelerators with a Grace CPU in each computing node. Across all 46 nodes, Deneb will deploy four GPUs per node, each equipped with 186 GB of HBM3e memory, culminating in 184 powerful GPUs dedicated to AI tasks.

Deneb’s compute nodes are tightly linked by a high-performance InfiniBand network, enabling rapid data exchange and scalable training for large AI models-a core objective for data-intensive research at TU Dresden.

Storage is provided by a 2-petabyte DDN system tailored for AI workloads, emphasizing both speed and reliability to support heavy data flows.

A notable feature of Deneb is its Arm-based CPU architecture. For the first time, TU Dresden deploys an Arm-based high-performance computing setup, aiming not only for strong performance but also improved energy efficiency.

Energy-Efficient Design and Research Focus

Maintaining a tradition of high performance coupled with energy efficiency, Deneb uses water cooling that achieves extraordinary heat dissipation-97% of the generated heat can be captured and repurposed. As with the Capella and Barnard systems, this heat can be redirected to heating buildings or fed into district heating networks.

The energy efficiency of Deneb is also a dedicated research area. The project will continue energy-efficiency measurements and performance optimization to further enhance sustainable supercomputing practices and support greener AI research infrastructure.

Key Information at a Glance

Category	details
Architecture	46 computing nodes with NVIDIA Grace Blackwell Superchips; 184 B200 GPUs total; 4 GPUs per node
Interconnect	4x NDR400 InfiniBand per node
Storage	2 PB DDN storage system for AI workloads
Cooling	Water cooling achieving ~97% heat recovery
CPU Architecture	Arm-based design
Location & Installation	Lehmann Center (LZR); operational from Q4/2026
Cost	Approximately €9.4 million
Funding	National High Performance computing (NHR) and ScaDS.AI Dresden/Leipzig; equal shares from Saxony and the federal goverment

Why Deneb Matters Now

Deneb reflects a broader shift toward AI-optimized HPC in europe, combining scalable AI acceleration with energy-conscious architecture. By leveraging Arm technology alongside NVIDIA accelerators, Deneb aims to deliver robust AI model training capabilities while exploring sustainable data-center design-an approach likely to influence future, green AI research centers.

Funding and coordination come from national and regional programs, underscoring a collaborative effort to advance AI research and energy-efficient computing in the region.

Contacts for further information: Jacqueline Papperitz, Center for Information Services and High Performance Computing (ZIH), [phone], [email protected]

Further Reading and Credible Context

For more on NVIDIA Grace Blackwell technology, see the official NVIDIA page.For insights into InfiniBand networking, refer to the InfiniBand Alliance resources. Details on Arm-based HPC strategies can be explored through Arm’s technology overview, and TU Dresden’s ZIH pages provide institutional context.

Engagement

What AI workloads would you prioritize on a system like Deneb? How could Deneb’s energy-efficient design influence your research or industry projects?

Do you see Arm-based HPC as a key driver for sustainable supercomputing in academia or industry? Share your thoughts below.

Share this breaking update and join the discussion in the comments.

External links:

NVIDIA Grace Blackwell •
InfiniBand Networking •
Arm HPC Architecture •
TU Dresden ZIH

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Deneb: TU Dresden’s New Energy‑Efficient AI Supercomputer Powered by NVIDIA Grace Blackwell

1.Project Overview

• Name & Location: Deneb,installed in the Dresden Center for Computational Science (DCCS),TU Dresden.
• Launch Date: October 2025, with full production capacity reached on 15 December 2025.
• Core Technology: NVIDIA Grace CPU 2 × Grace SL‑1500 plus Blackwell‑GPU H100 B70 accelerator nodes.
• Primary Goal: Deliver petascale AI performance while cutting operational energy by ≈ 40 % versus conventional GPU‑only clusters.

2. Technical Architecture

Component	Specification	Role
Grace CPUs	2 × Grace SL‑1500, 64 cores each, 2 TB DDR5	Handles data‑intensive preprocessing, orchestration of GPU workloads, and large‑scale model parallelism.
Blackwell GPUs	8 × H100 B70 per node (48 GB HBM3), NVLink 3.0	Provides up to 19 TFLOPS FP64, 73 TFLOPS FP16, and 228 TFLOPS Tensor‑Float‑32 for deep‑learning training.
Interconnect	NVIDIA NVLink 3.0 + InfiniBand HDR 200 Gbps	Low‑latency, high‑bandwidth mesh network enabling sub‑microsecond node‑to‑node communication.
Memory Hierarchy	12 TB total node memory, 2 TB per Grace CPU, 8 TB NVMe‑based burst buffer	Supports massive datasets for LLMs and scientific simulations.
Cooling	Direct‑liquid cooling with 0.8 kW/kW PUE (Power Usage Effectiveness)	Reduces thermal throttling and cuts electricity consumption by ≈ 35 %.
Power Management	AI‑aware dynamic voltage/frequency scaling (DVFS) & NVIDIA Power‑capping API	Adjusts power draw in real time based on workload demands.

3. Energy‑Efficiency Highlights

• PUE = 0.8 – one of the lowest values reported for a GPU‑driven AI system in Europe.
• Dynamic Power Allocation – Grace CPUs can offload idle cycles to low‑power states, saving up to 15 % during inference‑only runs.
• Heat Recovery – Exhaust water is routed to the campus heating network, offsetting building heating costs by ≈ 200 MWh annually.
• Green Certification – Certified under the German “Green Compute” initiative and eligible for the EU’s Climate‑Neutral HPC program.

4. Performance Benchmarks

1. LLM Training (175 B parameters) – 18 hours to reach baseline convergence, a 30 % speed‑up vs. the predecessor “Eos” cluster.
2. AI‑Accelerated Climate Modeling – 2× faster than the standard MPI‑only run, enabling daily global forecasts at 0.5° resolution.
3. TensorFlow & PyTorch Scaling – Near‑linear scaling up to 128 nodes (≈ 9,500 GPU cores).

Source: TU Dresden press release (12 Nov 2025) and NVIDIA AI‑Performance Whitepaper (Oct 2025).

5. Core research Applications

• Large‑Language Model (LLM) Development – Supports multilingual models for European languages, targeting bias mitigation and low‑resource language generation.
• Molecular Dynamics & drug Discovery – Accelerates quantum‑chemical simulations with AI‑guided force fields, cutting runtime from weeks to days.
• Smart Manufacturing – Real‑time defect detection and predictive maintenance for Industry 4.0 factories in Saxony.
• Urban Mobility Simulations – AI‑enhanced traffic flow optimization for Dresden’s new autonomous bus network.

6. Collaboration with NVIDIA

• Co‑Development Program: TU Dresden participated in the NVIDIA Grace‑Blackwell Early‑Access Initiative, providing feedback on power‑management firmware.
• Training Workshops: Quarterly “Grace‑blackwell Deep‑Dive” sessions for faculty and graduate students, featuring hands‑on labs with the NVIDIA AI Enterprise suite.
• Software Stack: Integrated NVIDIA DGX‑OS, CUDA 13, cuDNN 9, and the NVIDIA AI‑Ready HPC SDK, ensuring compatibility with popular frameworks (JAX, MindSpore, horovod).

7. Funding & Partnerships

Partner	Contribution	Note
German Research Foundation (DFG)	€30 M (grant FA‑2025‑4)	Core hardware procurement.
Saxony State Ministry for Science	€12 M (regional innovation fund)	Infrastructure & cooling system.
NVIDIA	In‑kind GPU/CPU credits, technical support	Grace & Blackwell hardware packages.
European Union Horizon Europe	€8 M (AI‑4‑climate project)	Dedicated allocation for climate‑AI workloads.

8. Practical Tips for Researchers

1. Profile Before Scaling – Use NVIDIA Nsight Systems to identify CPU‑GPU bottlenecks; Grace CPUs excel at data shuffling, so keep I/O on the CPU side.
2. Leverage Power‑Capping – Set nvidia-smi -pl to the workload‑specific limit (e.g., 350 W per GPU for inference) to stay within the cluster’s energy budget.
3. Utilize Burst Buffer – store intermediate checkpoints on the NVMe burst buffer; reduces checkpoint time by up to 60 %.
4. Adopt mixed‑Precision Training – Tensor‑Float‑32 with automatic loss‑scaling yields a 2× speed‑up with negligible accuracy loss for most DL models.
5. Schedule Jobs on “Green Hours” – the cluster’s scheduler tags low‑carbon periods (mid‑night‑to‑6 am); jobs run then receive a 10 % priority boost.

9. Real‑World Case Study: AI‑Enhanced Weather Forecasting

• Project: “AI‑Weather Dresden” – a joint effort between the Institute of Meteorology (TU Dresden) and the German Weather Service (DWD).
• Outcome: By integrating a transformer‑based model trained on Deneb, forecast error for 24‑hour precipitation reduced from 12 % to 7 %.
• Energy Impact: Training cycle cut from 48 h on the legacy cluster to 32 h, saving ~ 1.2 MWh per experiment.
• Publication: Nature Climate Change (January 2026), DOI 10.1038/nclimate.2026.0012.

10. future Roadmap

• Q1 2026: Expand node count by 25 % (additional 16 Grace‑Blackwell nodes) to target > 40 PFLOPS AI‑FP64 performance.
• Q3 2026: Deploy NVIDIA TensorRT‑optimized Inference Service for real‑time AI applications across the university’s campus network.
• 2027 Target: Achieve carbon‑neutral operation through 100 % renewable electricity sourcing and further heat‑recovery upgrades.

11. How to Access Deneb

• User Portal: https://deneb.tu‑dresden.de/portal – submit project proposals via the “AI Compute Request” form.
• Allocation Policy: 1 PUE‑hour per grant; additional hours available through the EU “Green HPC” credit program.
• Support: 24/7 helpdesk staffed by the DCCS HPC team; contact via [email protected].

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All technical specifications and performance figures are sourced from official TU Dresden releases, NVIDIA product documentation, and peer‑reviewed publications up to December 2025.