NAISS Medium Compute 2025

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Decided

This round has been closed as all proposals have been handled.

To apply, you must be a scientist in Swedish academia, at least at the level of assistant professor.

Deadlines and Decisions

Proposals are processed monthly during the year. Note that staff will be on vacation during the summer and Christmas and New Year holidays. Thus, July and December have different schedules than other months.

If Submitted Latest On	Expect Decisions Before	Note
2025-12-31	2026-02-01	Round close at New Year

Resources

Resource	Centre	Total Requested	Upper Limit	Default Storage	Available	Unit	Note
Alvis	C3SE	976 710	20 000		400 000	GPU-h/month	The Alvis resource is dedicated for AI/ML research.
The Alvis resource is dedicated for research in and research using AI/ML techniques. For general use of GPU:s instead use Dardel GPU. Allocations on Alvis will be scaled and transferred to the new NAISS system Arrhenius. It is NAISS's best estimate now that this will occur in the spring of 2026. More information will be announced as procurement and installation of Arrhenius progresses. The Alvis cluster is a national NAISS resource dedicated to Artificial Intelligence and Machine Learning research. Note: Significant generation of training data is expected to be done elsewhere. The system is built around Graphical Processing Units (GPUs) accelerator cards. The first phase of the resource has 160 NVIDIA T4, 44 V100, and 4 A100 GPUs. The second phase is based on 340 NVIDIA A40 and 336 A100 GPUs.
Mimer	C3SE	38 500	—	500	100 000	GiB
Allocations on Mimer will be scaled and transferred to storage on the new NAISS system Arrhenius. It is NAISS's best estimate now that this will occur in the spring of 2026. More information will be announced as procurement and installation of Arrhenius progresses. Project storage attached to Alvis and Vera, dedicated for AI/ML Mimer is an all-flashed based storage system based on as solution from WEKA IO. It consists of an 0.6 PB all-flash tier and a 7 PB Ceph based bulk storage tier (with spinning disk).
Tetralith	NSC	29 498	400		14 500	x 1000 core-h/month
Projects will receive a default 500 GiB storage allocation on Centre Storage at NSC. If you need more storage, please apply for a Storage project and decline default storage from this compute proposal. Allocations on Tetralith will be scaled and transferred to the new NAISS system Arrhenius. It is NAISS's best estimate now that this will occur in the spring of 2026. More information will be announced as procurement and installation of Arrhenius progresses. Tetralith is a general computational resource hosted by NSC at Linköping University. Tetralith servers have two Intel Xeon Gold 6130 processors, providing 32 cores per server. 1844 of the servers are equipped with 96 GiB of primary memory and 64 servers with 384 GiB. All servers are interconnected with a 100 Gbit/s Intel Omni- Path network which is also used to connect the existing storage. Each server has a local SSD disk for ephemeral storage (approx. 200GiB per thin node, 900GiB per fat node). An IBM Spectrum Scale system comprises the centre storage. 170 of the Tetralith nodes are equipped with one NVIDIA Tesla T4 GPU each as well as a high- performance NVMe SSD scratch disk of 2TB.
Centre Storage	NSC	48 500	—	500	60 000	GiB
If you need more than default storage, please apply for a Storage project and decline default storage from this compute proposal. Allocations on Centre Storage will be scaled and transferred to storage on the new NAISS system Arrhenius. It is NAISS's best estimate now that this will occur in the spring of 2026. More information will be announced as procurement and installation of Arrhenius progresses. Project storage for NAISS as well as LiU Local projects with compute allocations on resources hosted by NSC. Centre Storage @ NSC is designed for fast access from compute resources at NSC. It consists of one IBM ESS GL6S building block and one IBM ESS 5000 SC4 building block. In total there are 946 spinning hard disks and a small number of NVRAM devices and SSDs which act as a cache to speed up small writes. The total disk space that is usable for storing files is approximately 6.9 PiB.
Dardel	PDC	42 436	400		28 500	x 1000 core-h/month	Allocations on Dardel will be scaled and transferred to Arrhenius from 2027-01-01.
NAISS will terminate operation of Dardel on 2027-01-01 and allocation will be scaled and transferred to Arrhenius from that date. Dardel is a Cray EX system from Hewlett Packard Enterprise, based on AMD EPYC processors with an accompanying Lustre storage system. The nodes are interconnected using Slingshot HPC Ethernet.
Dardel-GH	PDC	2 800	1 000		20 000	GPU-h/month	Allocations on Dardel-GH will be scaled and transferred to Arrhenius from 2027-01-01.
NAISS will terminate operation of Dardel-GH on 2027-01-01 and allocation will be scaled and transferred to Arrhenius from that date. Dardel-GH is the accelerated partition based on nVIDIA GraceHopper GPUs. It has an accompanying Lustre storage system. The nodes are interconnected using Slingshot HPC Ethernet. A new high-performance GPU resource is now accessible on the Dardel supercomputer at PDC, providing 20,000 GPU hours per month. Eight additional nodes have been purchased, each equipped with four NVIDIA Grace Hopper (H100) GPUs and four NVIDIA Grace ARM CPUs—totalling 32 Grace Hopper Superchips. This state-of-the-art platform combines the performance of the Hopper GPU with the energy-efficient Grace ARM CPU, designed for demanding AI and high-performance computing (HPC) workloads. The Grace Hopper resource is exclusively available to projects under the Medium compute category, with priority given to users running codes not currently supported on Dardel's AMD GPUs. If there is a need to run unique or specialized software, this new system offers a significant opportunity for improved performance. Important: For users with an existing Medium project on Dardel with a need to use dardel-GH resource, submit a continuation of your existing Medium project (you can submit a continuation event if your project is not finished). For further details or support with the application process, please contact the NAC-WG.
Dardel-GPU	PDC	100 091	4 000		106 000	GPU-h/month
These GPUs are not nVIDIA GPUs but rather AMD GPUs, so if your software runs using CUDA, a certain amount of conversion of the code is needed. You can read information about this at https://www.lumi-supercomputer.eu/preparing-codes-for-lumi-converting-cuda-applications-to-hip/ Reporting on GPU consumption on Dardel is not working yet. Dardel-GPU is the accelerated partition based on AMD’s Instinct MI250X GPU of the Cray EX system from Hewlett Packard Enterprise. It has an accompanying Lustre storage system. The nodes are interconnected using Slingshot HPC Ethernet.
Klemming	PDC	77 500	—	500	300 000	GiB	Allocations on Klemming will be scaled and transferred to Arrhenius from 2027-01-01.
More information about project directories in Klemming can be found at https://www.pdc.kth.se/support/documents/data_management/lustre.html. NAISS will terminate operation of Klemming on 2027-01-01 as the compute resources Dardel, Dardel-GPU and Dardel-GH will be terminated on that date. Allocations will be transferred to Arrhenius from that date. Project storage for NAISS as well as PDC projects with compute allocations on resources hosted by PDC. Klemming is designed for fast access from compute resources at PCD. It uses the Lustre parallel file system, which is optimized for handling data from many clients at the same time. The total size of Klemming is 12 PB.
Cloud	SSC	150 000	—		1 000 000	Coins
Allocations on Cloud will be scaled and transferred to the new NAISS system Arrhenius. It is NAISS's best estimate now that this will occur in the spring of 2026. More information will be announced as procurement and installation of Arrhenius progresses. Swedish Science Cloud (SSC) is a geographically distributed OpenStack cloud Infrastructure as a Service (IaaS), intended for Swedish academic research provided by NAISS. It is available free of charge to researchers at Swedish higher education institutions through open application procedures. The SSC resources are not meant to be a replacement for NAISS supercomputing resources (HPC clusters). Rather, it should be seen as a complement, offering advanced functionality to users who need more flexible access to resources (for example more control over the operating systems and software environments), want to develop software as a service, or want to explore recent technology such as for “Big Data” (e.g. Apache Hadoop/Spark) or IoT applications. Examples of applications that must run in the normal HPC clusters since the SSC isn't built for those purposes. AI/LLM/Deep Learning and other applications relying on GPU resources cannot be run in the SSC due to the limited access to GPU's. Applications assuming high performance or large volumes of storage, network, or cores. Benchmarking will not perform well within SSC due to performance limitations. SSC isn't classified for Sensitive data, those project MUST use Cluster system for sensitive data within NAISS.

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