Prime Intellect

An integrated compute, training, inference and sandbox stack for building your own AI models

Visit site ↗

10.3MB · 10,798px tall · uses ABC Favorit Mono, Geist, Geist Mono

Industry

AI Infrastructure, Devtools

Style

Dark Mode, Bento, Minimal

Stage

Series A

Built with

Next.js

About the site

Prime Intellect sells an open stack for companies that want to own their models: RL environments and evaluations, hosted post-training, inference on its own infrastructure and on-demand GPU compute. Its homepage is dark and grid-ruled with mono labels, opening on 'Own Your Intelligence' beside a glowing glass monolith in a misty jungle, a 'pip install prime' prompt and backers from Founders Fund and NVIDIA to Andrej Karpathy. A two-row logo grid of case studies (Ramp, NVIDIA, Zapier) leads into the Lab platform, laid out as numbered figures (RL environments, evaluations, a reward curve for hosted training, inference), then a neon-yellow quote from Ramp's co-CEO. Inference, Compute and Research each open on generative visuals (a green particle field, a spectrum of vertical bars, pixel-art posts), with live GPU prices, a reserved-cluster calculator and customer stories before a hiring grid of team avatars. A useful reference for making deep ML infrastructure feel like a research lab through technical-drawing art direction.

How this page sells

For AI teams tired of renting frontier models: sell ownership of your own models, backed by researcher-grade proof and every layer of the stack.

Primary navigation

Training
Inference
Compute
Research
Docs
Blog
Careers
Book a call
Login
Start training

ClickTap a section to see it on the page

'Own Your Intelligence' sits beside a glowing glass monolith in a misty jungle, with a 'pip install prime' prompt and a 'Backed by' line naming Founders Fund, NVIDIA, Intel, Andrej Karpathy and John Schulman.
Why it converts: Ownership is the emotional pitch against renting closed models, and the install prompt plus researcher backers speak straight to engineers.
A two-row grid of logos (Ramp, NVIDIA, Zapier, Extropic, Goodfire, Inception, Arcee, Browserbase), with 'Case study' and 'Read more' tags on the ones that have stories.
Why it converts: AI labs and AI-native companies as customers say the stack is good enough for people who could build their own.
'Lab. Post-train your own self improving agents' lays the product out as four figures: an init-develop-eval-push loop for RL environments, hosted evaluations, a reward curve with training parameters, and inference.
Why it converts: Diagrams styled like a research paper show the actual workflow, which builds trust with ML engineers who distrust marketing screenshots.
A neon-yellow card quotes Ramp's co-CEO: a small RL-trained subagent 'beat the frontier models on accuracy while running at faster speeds and a fraction of the cost', beside a Zapier engineer on evals.
Why it converts: A named executive saying their own model beat frontier models is the exact outcome the page promises, from a buyer peers respect.
'Environment Hub' offers 2,500+ open-source RL environments next to a live-looking hub of starred environments, above cards for the Verifiers library, Prime-RL and Sandboxes with code snippets.
Why it converts: A large open-source community and real code signal momentum and lower lock-in risk, which matters to teams choosing a training platform.
'Inference. Run GLM-5.3 and other leading models through one OpenAI-compatible API.' over a green particle field, then cards for hosted models, a third-party gateway and dedicated serving.
Why it converts: An OpenAI-compatible API means teams can switch with a config change, removing the biggest practical barrier to trying it.
'Compute.' opens on a spectrum of vertical bars, then live GPU prices (B300 at $4.99/hr, H100 at $2.43/hr) and a reserved-cluster calculator showing $20,183,040 profit from reselling idle capacity.
Why it converts: Public prices and a resale model are unusual for GPU clouds, and the profit figure reframes a reserved cluster as an asset.
'Research. Our Contributions to the Frontier of Open-Source AI' features a pixel-art Prime Agent post, INTELLECT-3 (a 100B+ MoE trained with RL) and a row of recent research posts.
Why it converts: Training and releasing their own frontier models proves the team can do what it sells, earning authority no testimonial can.
Three story cards: 'How Zapier Turned AutomationBench Into a Continuous Agent Improvement Loop', how Ramp used RL to beat frontier models at spreadsheet search, and how Goodfire catches AI models cheating.
Why it converts: Each story maps to a different product (post-training, evaluations), letting buyers find the use case closest to theirs.
'Join Prime Intellect' beside a grid of team avatars, then a footer with a dithered 'Be your own Lab.' and the same Start training and Book a call buttons as the hero.
Why it converts: The team grid makes a deep-tech company feel human, and ending on 'Be your own Lab' restates ownership right beside the two ways to start.