Research Focus
Marlowe enables frontier-scale AI research across Stanford, from 80-billion-parameter brain models to genomic foundation models to autonomous driving systems. Watch the researchers, read their stories, and meet the community pushing the boundaries of what's possible with GPU computing.
Computer music · 1976
Leland Smith with computer music displayed on a CRT monitor · Stanford, 05/19/1976
From the lab
Counterfactual World Modeling: Training Brain-Scale Neural Networks
Validated 80B-parameter brain-inspired neural network training on Marlowe in a February 2026 proof-of-concept. Now training PSI2-30B — a 30-billion...
Read full story →
The Enigma Project: First Foundation Model and Digital Twin of the Brain
Trained the first foundation model of mammalian visual cortex on Marlowe — a 2B-parameter multimodal transformer on recordings from 3 million neuro...
Read full story →
AI Virtual Cell: Genomic Foundation Models at Frontier Scale
Building the molecular foundation of the AI Virtual Cell — novel reasoning architectures designed for biological data modalities (DNA, RNA, protein...
World Models for Cancer Biology
Published a vision-language foundation model in Nature (January 2025) and AI-enabled virtual spatial proteomics in Nature Medicine (January 2026) f...
Vision-Language Foundation Models for Radiology
We are building a vision-language foundation model for medical image interpretation, trained on Stanford's 2 petabytes of radiology data. Our first...
Video World Models for 3D Scene Understanding and Generation
Fine-tuning billion-parameter video diffusion models on Marlowe for spatially aware scene understanding and generation. Two active projects: real-t...
The research community
Iro Armeni
4D Scene Understanding: AI for Dynamic Real-World Environments
Two active projects in 4D scene understanding on Marlowe. ReScene4D introduces temporally consistent instance segmentation from sparse 3D scans — a novel task for construction digital twins and embodied AI. A companion project develops end-to-end 4D reconstruction and camera localization from monocular video using DiT architectures for autonomous navigation and AR/VR.
Emmanuel Candès
Frontiers of AI Scaling: Synthetic Data and Test-Time Reasoning
As the finite pool of internet text that powered a decade of AI scaling runs dry, Candès's group uses Marlowe to chart the next frontiers — generating synthetic training data and scaling reasoning at test time. That work includes s1 (Simple Test-Time Scaling), a low-cost open reasoning model built in part on Marlowe and featured in The Economist. Running through all of it is a distinctively statistical agenda: knowing when an AI's output can be trusted, and how to correct it when it cannot.
Thierry Tambe
Efficient AI Computing: From Model Compression to Edge-Deployable Video Generation
Extending BlockDialect (ICML 2025), their fine-grained mixed-format quantization method, from LLMs to video diffusion transformers — enabling real-time video generation on edge devices by quantizing models like Open-Sora 2.0. Also developing compact visual encoders using 2D Gaussian splatting for vision-language models. One of Marlowe's most active research groups.
Brian Hie
Beyond Evo 2: Next-Generation Biological Models
Creators of Evo 2, the state-of-the-art DNA language model published in Nature, now developing next-generation biological models on Marlowe to address limitations in protein structure prediction and fitness tasks that scale alone cannot solve. Training models at 150M parameters before scaling to 1B, using training and inference infrastructure that has been used to scale models through 40B parameters.
James Zou
AI Agents for Biomedical Discovery: Self-Improving LLMs with Scientific Tools
Developing algorithms that enable large language models to self-improve and learn to use scientific tools like AlphaFold and biomedical databases to build deeper expertise. Fine-tuning and evaluating LLMs (7B-70B parameters) for high-impact applications in healthcare, biology, chemistry, and medicine.
Azalia Mirhoseini
Personal and Efficient Local AI
OpenJarvis is an open-source framework for personal AI that runs entirely on personal devices, keeping user data local and calling the cloud only when truly necessary. While OpenJarvis performs inference on-device, the specialized small models that make local-first AI practical must first be trained at scale, and Marlowe provides the compute backbone for exactly this. We use Marlowe to distill and fine-tune compact, task-specialized language models that recover much of the capability of far larger cloud models while fitting within the strict latency, memory, and energy budgets of consumer hardware (NVIDIA GPUs, AMD GPUs, and Apple Silicon).
Marlowe on film
Marlowe: Compute for Discovery
Interested in using Marlowe for your research?
Principal Investigators new to Marlowe are eligible for 5,000 free GPU-hours. Get started with your first allocation.