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Getting started

Spike is not a self-service cluster where an account is created immediately after a form is submitted. Access starts with a conversation. For TU/e academic research (Spike 1), the Supercomputing Center (SCC) reviews the project and capacity. For commercial and enterprise computing (Spike A), Supercomputing Brabant (SCB) guides the intake and onboarding. In both tracks, an assigned engineer onboards you personally.

Before starting, identify which platform track applies to your project:

Capability / Service Spike 1 (TU/e Academic Research) Spike A (Supercomputing Brabant Commercial)
Target audience TU/e researchers, postdocs, PhDs, students Commercial enterprises, industry R&D teams
Compute hardware Dedicated DGX B200 nodes (8x B200 GPUs) Dedicated DGX B300 nodes (8x B300 GPUs, 256 cores, 2.3 TB RAM)
Parallel storage 250 TiB DDN project space with S3 option All-flash VAST Data NVMe parallel cluster (/data or /vast)
Engineering support TU/e Supercomputing Center (SCC) AI Engineers Supercomputing Brabant (SCB) Engineers
Hosted LLM gateway Available (hosted frontier models) Not provided (companies deploy custom inference/models)
Remote data transfer TU/e network shares, NetApp, Snellius (SURF), S3 Corporate on-prem, cloud storage (AWS/Azure/GCP) over VPN
Onboarding route TopDesk Service Desk request Supercomputing Brabant business development & dual-gate intake
  1. Submit an access request. Use the Service Desk form as the first step. If your department already tracks the proposal in the Research Cockpit, include that reference in the request instead of creating an unrelated second record. Describe the research question, the expected GPU workload, your data, and the period in which you need the system.
  2. Discuss the project with an SCC AI engineer. During the intake, you will look at eligibility, technical fit, data handling, expected scale, and whether Spike offers a real advantage over another computing platform.
  3. Receive a capacity decision. If the project is a good fit and the cluster has room, onboarding can be scheduled. If the cluster is full, SCC will put the project on the waiting list rather than promise capacity that is not available.
  4. Onboard together. Set aside part of a day with an AI engineer. You will learn the Spike-specific workflow, including projects, containers, loading data, storage, submitting work, and checking a running workload.
  5. Start with an engineer nearby. After onboarding, you will remain in close contact with an SCC AI engineer while you bring up the first real workloads, resolve early problems, and learn to use the system efficiently.

The onboarding session teaches you how to work on Spike; it is not an introduction to machine learning, Linux, Python, or your research software. Before applying, have:

  1. Code that is close to runnable on HPC. It does not need to be packaged perfectly for Spike yet, but it should not still require weeks of development before a meaningful test can run. SCC understands that moving into containers and onto B200 GPUs takes some adjustment.
  2. An estimate of the dataset size. The data should be ready to transfer, and you should know roughly how much needs to move onto the platform.
  3. A project name. Use one name consistently in the access request and during onboarding.
  4. A list of users. Identify everyone who will need access to the project.
  5. A documented data decision. Use Data classification on Spike to describe the dataset and complete any Data Steward, ethics, privacy, or agreement review before transfer.

You should also understand your own code and be comfortable working from a terminal. Container experience is helpful, but SCC can help translate an otherwise working environment into the Spike workflow.

The Access and intake guide explains what makes a strong request, how SCC assesses fit, and what to prepare for the intake and onboarding sessions.

Use these guides with your SCC contact while you prepare the first real run: