Enterprise AI Analysis
INTELLECT-3: Scaling RL for Frontier Models
INTELLECT-3 is a 106B-parameter Mixture-of-Experts model trained with large-scale reinforcement learning, achieving state-of-the-art performance in math, code, science, and reasoning benchmarks, outperforming many larger frontier models. This report details the full infrastructure stack, including prime-rl for asynchronous RL, verifiers for LLM environments, and Prime Sandboxes for secure code execution, enabling training on 512 H200s. The model and infrastructure are open-sourced.
Executive Impact
The INTELLECT-3 model demonstrates significant advancements in AI capabilities, offering unparalleled performance for complex enterprise applications.
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AIME 2024 Score
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LiveCodeBench v6 Score
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H200 GPUs Utilized
2 Months
Training Duration
Deep Analysis & Enterprise Applications
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RL Infrastructure
Model Training
prime-rl Training Architecture
Rollout Generation (Inference)
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Orchestration & Batching
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Policy Update (Trainer)
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New Weights Notification
Asynchronous Off-Policy Training
0x Faster Throughput Increase with Async Off-Policy RLOverlapping rollout generation and training on disjoint GPUs significantly boosts end-to-end system throughput. This approach prevents inference engines from stalling while waiting for updated policies, allowing for continuous data flow and faster iteration times.
| Feature | Naive Orchestration | Prime Sandboxes |
|---|---|---|
| Execution Latency | Seconds (API server bottleneck) | Milliseconds (Bypasses K8s API) |
| Scalability | Unscalable at high concurrency | High-throughput, thousands of concurrent rollouts |
| Security | Standard container isolation | gVisor user-space kernel isolation |
| Image Distribution | Slow (Docker Hub rate limits) | Fast (Custom Registry, Image Streaming, Warm Pools) |
INTELLECT-3 on AIME 2024/2025
INTELLECT-3 achieved remarkable performance on challenging math benchmarks. Scoring 90.8% on AIME 2024 and 88.0% on AIME 2025, it surpassed DeepSeek's frontier models and matched GLM-4.6, despite having over 3x fewer parameters. This demonstrates the effectiveness of our end-to-end RL training pipeline and specialized math environment.
AIME 2024 Score: 90.8%
AIME 2025 Score: 88.0%
Scaling Sequence Length
0 Tokens Max Sequence Length AchievedThrough activation offloading to CPU and aggressive checkpointing, INTELLECT-3 was trained effectively on sequences up to 72,000 tokens. This is crucial for long-context reasoning in complex agentic environments, ensuring the model maintains consistency across extended tasks.
Advanced ROI Calculator
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Potential Annual Savings
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Hours Reclaimed Annually
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Your Implementation Roadmap
A structured approach to integrating INTELLECT-3 into your enterprise.
Phase 1: Foundation & Infrastructure
Set up the prime-rl framework and Environments Hub for scalable RL training.
Phase 2: Supervised Fine-Tuning
Execute SFT on GLM-4.5-Air base with diverse datasets for reasoning and agentic skills.
Phase 3: Large-Scale Reinforcement Learning
Train with online difficulty filtering and in-flight weight updates on 512 H200s.
Phase 4: Continuous Optimization
Iteratively improve model performance with new RL environments and agentic tasks.
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