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Prime Intellect Profile picture
@PrimeIntellect
Sep 15 • 4 tweets • 1 min read • Read on X
Today we're launching Reserved Instances

- Request 8–1,000+ GPU clusters
- Get quotes from up to 50+ providers in 24h
- Re-sell idle GPUs back to our spot market
- Support from our research team Image
Expanding our Compute Exchange

- Find the best and most cost-effective reserved instance offers across 50+ providers
- Re-sell idle GPUs from your reserved cluster on our liquid compute market
- H100s, H200s, B200s, and NVL72 clusters available today
Additional Features

- Orchestration with SLURM, Ray or Kubernetes
- Monitoring with Grafana dashboards
- Native integrations into our full-stack infra offering: Environment Hub, Sandboxes, Reinforcement Fine-Tuning, Multi-Node Training
- Dedicated support from our research team
Submit a request today:

app.primeintellect.ai/dashboard/quot…

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More from @PrimeIntellect

Prime Intellect Profile picture
Sep 6
Environments Hub launched a week ago, and we’ve already crowdsourced 100+ environments.

Ranging from theorem proving, kernel generation, scientific qa, browser-use, and more. Every environment contributed shifts the balance of power towards open-source AI.

Some highlights: Image
Lean 4 Theorem Proving

Multi-turn formal theorem proving in Lean 4, where models alternate between reasoning, sketching proof code, receiving feedback.

Ideal for search guided rl, process rewards, and curriculum design.

By @myainotez

app.primeintellect.ai/dashboard/envi…
Kernel Bench

GPU kernel generation under verifiers. Tests systems level code generation for correctness and performance under CUDA constraints.

By Fido WANG

app.primeintellect.ai/dashboard/envi…
Read 26 tweets
Prime Intellect Profile picture
Sep 3
Join the Prime Intellect RL Residency

Our community already shipped 100+ environments to the Environment Hub

Help us accelerate, with compute, a stipend, and support from our research team Image
The RL Residency gives you:
— Compute for experiments
— A stipend
— Hands-on support from our internal research team
Who should apply?
— Grad students with research ideas
— Independent builders & hackers
— Part time researchers exploring novel RL environments and evals

If you’ve wanted to build environments but lacked compute or support - this is for you
Read 7 tweets
Prime Intellect Profile picture
Aug 27
Introducing the Environments Hub

RL environments are the key bottleneck to the next wave of AI progress, but big labs are locking them down

We built a community platform for crowdsourcing open environments, so anyone can contribute to open-source AGI
Environments are where agents learn.

They define the world, rules, and feedback loop of state → action → reward. Everything from coding/math tasks to games and multi-turn dialogue evals can be thought of as environments. Without them, RL is just math with nothing to interact with.
This is why environments are pivotal to the next wave of AI progress.

But while big labs are spending millions buying and privatizing RL environments, open-source has no comparable way to crowd-source them at scale.

We’re building the platform and infrastructure to change that.
Read 10 tweets
Prime Intellect Profile picture
Jun 23
Launching SYNTHETIC-2: our next-gen open reasoning dataset and planetary-scale synthetic data generation run.

Powered by our P2P inference stack and DeepSeek-R1-0528, it verifies traces for the hardest RL tasks.

Contribute towards AGI via open, permissionless compute.
Planetary-Scale Inference
Our peer-to-peer decentralized inference stack moves into production, enabling everyone—from consumer GPUs to hyperscale clusters—to contribute meaningfully towards open-source AI progress.Image
Pipeline Parallelism
No single GPU holds the full model - each handles a stage, streaming activations forward. This lets smaller GPUs run large models like DeepSeek-R1. Hidden states pass stage to stage; the final GPU decodes a token, sends it back, and the cycle continues.Image
Read 10 tweets
Prime Intellect Profile picture
May 12
Releasing INTELLECT-2: We’re open-sourcing the first 32B parameter model trained via globally distributed reinforcement learning:

• Detailed Technical Report
• INTELLECT-2 model checkpoint

primeintellect.ai/blog/intellect…
To train a model with reinforcement learning in a fully decentralized setting using community-contributed GPUs, we open-source several novel infrastructure components. Image
PRIME-RL: A fully asynchronous reinforcement learning framework designed for decentralized training. Decoupling of rollout generation, model training, and weight broadcasting enables training across heterogeneous, unreliable networks.
Read 12 tweets
Prime Intellect Profile picture
Apr 15
Today we’re launching INTELLECT-2:

The first decentralized 32B-parameter RL training run open to join for anyone with compute — fully permissionless.

Scaling towards frontier reasoning across coding, math and science.
INTELLECT-2 brings decentralized training into the inference-time compute era:
• Fully async, decentralized reinforcement learning
• Eliminating communication overhead
• Scalable across heterogeneous GPUs worldwide

primeintellect.ai/blog/intellect…
Over the past months, we’ve built the full open-source stack to enable INTELLECT-2:
• PRIME-RL: fully async decentralized RL
• GENESYS & SYNTHETIC-1: crowdsourced tasks & verifiers for RL
• TOPLOC validation: verifiable inference with low overhead
• Protocol Testnet: global AI coordination infrastructureImage
Read 8 tweets

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