About Marlowe

Marlowe

Stanford's first GPU-based computational instrument: 248 NVIDIA H100 GPUs powering frontier AI research across all seven schools, managed by HAI in partnership with VPDoR and UIT. Marlowe is people as much as hardware — a research data science team partners directly with faculty to turn that hardware into science.

Marlowe NVIDIA H100 GPU racks with the Marlowe noir-detective emblem

Marlowe is housed at SLAC

GPU-Based Computational Instrument

Marlowe: From film noir detective to frontier AI infrastructure

Named after Philip Marlowe, the film noir detective, Marlowe is Stanford's first large-scale GPU computational instrument, designed to give faculty the infrastructure to train foundation models, run large-scale simulations, and pursue computational work at scales previously available only to industry.

A team of Research Data Scientists partners directly with faculty to optimize code, scale training across multiple nodes, and maximize the scientific return from every GPU-hour allocated.

  • Partner with faculty to design and execute GPU-accelerated research
  • Optimize training pipelines for multi-node scaling
  • Integrate open science practices into computational research
  • Provide technical consulting on model architecture and distributed training
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Technical Specifications

GPU 248x NVIDIA H100 80GB SXM5
Nodes 31 (8 GPUs per node)
CPU 2x Intel Xeon Platinum 8480C (112 cores/node)
Memory 2 TB RAM per node
Interconnect NVIDIA InfiniBand NDR (400 Gb/s)
Storage 5.5 PB total (2.5 PB parallel scratch + 3 PB project/home)
GPU Interconnect NVLink + NVSwitch (intra-node)
Data Classification Low & Moderate Risk
Research Data Science Team

The people behind Marlowe

A team of research data scientists and program staff who partner directly with Stanford faculty — optimizing code, scaling training across nodes, and turning GPU-hours into scientific results on Marlowe.

Emmanuel Candès

Emmanuel Candès

Marlowe Faculty Director

Emmanuel Candès is the Barnum-Simons Chair of Mathematics and Statistics at Stanford and the faculty director of Marlowe. One of the most influential statisticians of his generation, he is a pioneer of compressed sensing and modern distribution-free methods for trustworthy prediction. As Marlowe's faculty lead, he sets the instrument's scientific direction and champions frontier-scale GPU computing for researchers across all seven of Stanford's schools.

Craig Kapfer

Craig Kapfer

Senior Director, Research Data Science · Marlowe Lead

Craig Kapfer leads Research Data Science at Stanford HAI and directs Marlowe. He has spent his career building scientific computing programs that support frontier research, with prior leadership roles at the Chan Zuckerberg Biohub, GSK, and KAUST. He holds master's degrees in Mathematics and Computer Science from Indiana University Bloomington.

Balasubramanian Narasimhan

Balasubramanian Narasimhan

Senior Research Scientist

Balasubramanian Narasimhan is a research scientist with joint appointments in HAI, Biomedical Data Science, and Statistics. He earned his doctorate from Florida State University under George Marsaglia; his interests span machine learning, high-performance and distributed computing, and reproducible research. He is an elected member of the R Foundation.

Casey Fleeter Masuda

Casey Fleeter Masuda

Research Data Scientist

Casey Fleeter Masuda is a research data scientist and computational mathematician. She earned her PhD at Stanford's Institute for Computational and Mathematical Engineering under Alison Marsden, applying mathematical modeling and uncertainty quantification to cardiovascular fluid dynamics, then held a postdoctoral fellowship at Calico Life Sciences on mechanistic models of aging. She holds a BA in Physics from Harvard.

Koji Abe

Koji Abe

Research Data Scientist

Koji Abe is a research data scientist and computational biologist focused on reliable, reproducible machine-learning workflows for biomedical research. At Stanford Medicine's Human Immune Monitoring Center he built scalable multi-omics biomarker pipelines on Marlowe; he now supports researchers with GPU/HPC workflows, performance debugging, and reproducibility. He holds a PhD in Bioengineering from the University of Washington.

Marcelo Alvarez

Marcelo Alvarez

Research Scientist

Marcelo Alvarez is a research scientist with joint appointments at KIPAC and HAI. His scientific research includes cosmic reionization, large-scale structure, and the cosmic microwave background. Marcelo has served as a research data scientist for Marlowe since 2025. Most recently, his work has extended to developing reproducible AI-accelerated data analysis workflows for next-generation cosmology surveys.

Sophia C. An

Sophia C. An

Program Manager

Sophia C. An is the program manager for Marlowe. She came to research computing from UX and product design — at Samsung, where she co-created the award-winning Frame TV with Yves Béhar, and at Amazon Lab126 — and recently led federally funded research at UC Irvine in partnership with the DOE, NASA, DoD, and DARPA.

Video

Inside Marlowe

Video

Stanford Data Science Presentation