Modal has raised an $87M Series B led by Lux Capital. Read more

High-performance GPU Notebooks

Modal Notebooks combine an intuitive interface with near-instant cold starts. This gives you the flexibility to run everything from lightweight experiments to large-scale multi-GPU training jobs.

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“Testing different GPUs on the fly has massively accelerated our workflow for profiling models. Tasks that used to take days now take minutes.”

Simran Makariye, Machine Learning Engineer

“Other platforms like Colab didn't have the capacity we needed. We love how Modal Notebooks lets us scale up any amount of GPUs and compute.”

Arda Göreci, Co-founder & CTO

“We used to spend hours just setting up access and dealing with surprise costs. With Modal Notebooks, I set up a research environment for our intern and could give feedback on his experiment in real time.”

Alice Yu, Co-Founder & CEO

Go from idea to running code in seconds

Instant cold-starts

Start a notebook running ML on high-performance GPUs in under 5 seconds.

Fast iteration

Run code, see results, and refine ideas in real time, with the responsiveness needed for research and exploration.

Swap GPUs on the fly

Switch from CPUs to up to 8 Nvidia H100s or B200s in just one click.

Built on massively scalable infrastructure

Petabyte-scale storage

Use Modal's global distributed file system (Volumes) to work with massive datasets.

On-demand GPUs priced by the second

Access the latest GPUs without reservations. Pay only for what you use, down to the second.

Leverage the rest of the Modal stack

Notebooks can call Modal Functions and share Volumes and Secrets with all of your Modal Apps.

Designed by developers, for developers

Real-time collaboration

Multiple cursors, live edits, and shared context like in Google Docs—no more emailing .ipynb files.

Automatic idle shutdown

Never waste money on a kernel you forgot to close down again, or configure if you need control.

Modern AI code editing

Pyright type checking, rich outputs for visualizations, and AI-powered edit suggestions help you move fast.

“Modal Notebooks have been incredible for sharing ML research across Suno. Engineers and designers can start playing with cutting-edge models minutes after they're finished training.”

Victor Tao, ML Research Engineer

Featured Examples

Whisper Audio Analysis

Transcribe audio with OpenAI's Whisper, and visualize how it attends to different parts of the input via attention weights over time.

Exploring Qwen3 on vLLM

Run a fine-tuned open source code generation model. Compare code quality, analyze token patterns, and experiment with different prompts.

Parse Documents with dots.ocr

Extract and analyze text from images and PDFs, using the state-of-the-art OCR model based on LLM foundations.

UMAP Embeddings Visualization

Visualize high-dimensional embeddings in 2D/3D space. Explore semantic similarity, cluster data, and understand vector representations.

Ship your first app in minutes.

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$30 / month free compute