Chalk for

Chalk for Researchers

Run next-gen AI projects and get from hypothesis to results faster. Chalk for Researchers gives academic labs and research teams access to GPU capacity and a complete environment for experiments on real-world data.

Trusted by leading teams

What academics and research teams get

Compute, tooling, and amplification for research, in one program built around helping you solve ambitious problems.

GPU capacity when you need it

Run inference and experiment workloads on GPUs, without waiting in a queue. Chalk gets you access free.

A complete research environment

Full access to Chalk Sandboxes, Model Gateway, Traces and Evaluations, Notebooks, and the MCP Gateway. Fine-tune models, run evaluations, or build agents in one place.

Visibility for your work

When you publish a paper, release a dataset, or host an event, we help share it with the wider AI community.

Who can apply

You're an academic, lab, or part of a research program pushing AI and ML forward.
Your experiments need GPU compute and fresh, real-world data.
You need a secure way to connect academic, institutional, or sensitive data sources to your models and agents.

Not sure you're a fit? Tell us about your work and we'll figure it out together.

How it works

Tell us about your work.

Share your program, the courses or labs you’re supporting, and the kind of projects you and your team of students or researchers are working on.

Scoping call.

We work through your use case, compute needs, and workloads, and map out where Chalk can help.

Launch and publish.

We provide the resources and infrastructure you need for free, so you can start your project and publish results fast.

Built for next-gen AI research

Chalk for Researchers is designed to meet you where you are, from early-stage projects to institution-wide programs. We'll walk through the details on your scoping call.

What researchers build on Chalk

Fine-tune Models on Your Data

Train and fine-tune open models on your proprietary data with Chalk.

Agent Evals

Evaluate agents against time-consistent production data as it actually was. Chalk replays a deployed agent against a dataset of real historical context, with one knowledge cutoff applied to every tool call. Each run pins its dataset, task and scorer versions for consistency, and no eval data leaves your cloud environment.

Autoresearch Agents

Recursive self-improvement loops for the ML lifecycle.

Fine-tune Models on Your Data

Train and fine-tune open models on your proprietary data with Chalk.

Agent Evals

Autoresearch Agents

Recursive self-improvement loops for the ML lifecycle.

Fine-tune Models on Your Data

Train and fine-tune open models on your proprietary data with Chalk.

Agent Evals

Autoresearch Agents

Recursive self-improvement loops for the ML lifecycle.

FAQ

Labs, research groups, and academic programs that are actively building with AI or ML, who need data infrastructure and resources to run projects fast.

Ready to talk about Chalk for Researchers?

Share your research and goals. We’ll follow up to talk through how Chalk can support your work.