Prime Intellect raised $20M promising decentralized AI training. Its flagship model trained on a centralized 512-GPU cluster instead. The gap between vision and execution reveals hard truths about frontier AI infrastructure.
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Prime Intellect raised $20M promising decentralized AI training. Its flagship model trained on a centralized 512-GPU cluster instead. The gap between vision and execution reveals hard truths about frontier AI infrastructure.
Prime Intellect built its brand on a promise: decentralized AI training that could rival the tech giants. Founded in early 2024 by Johannes Hagemann and Vincent Weisser, the San Francisco-based startup raised $20.5 million from Founders Fund, CoinFund, and a roster of AI luminaries including Andrej Karpathy and Clem Delangue. The pitch was elegant. Aggregate idle GPUs from around the world, develop frameworks that make distributed training efficient, and build open models that compete with closed-source labs.
Last week, the company released INTELLECT-3, a 106-billion-parameter Mixture-of-Experts model that scores 90.8% on AIME 2024 and beats Z.ai's own post-trained GLM-4.5-Air by 8 percentage points on LiveCodeBench. The numbers are real. But the story behind them reveals a company navigating the gap between its founding vision and the brute force realities of frontier AI.
The Breakdown
• INTELLECT-3 is a post-train on Z.ai's GLM-4.5-Air base model, not trained from scratch, costing roughly $150K in compute
• Despite its decentralized AI branding, the model trained on a traditional 512-GPU H200 cluster with InfiniBand networking
• Benchmarks beat DeepSeek-R1 on AIME 2024 (90.8% vs 83.2%) but fall short of Z.ai's own next-gen GLM-4.6 at 92%
• Prime Intellect's real product is infrastructure: prime-rl framework, Environments Hub, and Prime Sandboxes for RL training
INTELLECT-3 is not trained from scratch. The model starts with GLM-4.5-Air-Base, a foundation model released by Chinese AI company Zhipu AI (operating globally as Z.ai) in July 2025. GLM-4.5-Air itself carries 106 billion total parameters with 12 billion active at inference, built on a Mixture-of-Experts architecture optimized for reasoning and tool use. Z.ai pre-trained the base on 22 trillion tokens, including 7 trillion devoted specifically to code and reasoning. Prime Intellect inherited all of that work.
The post-training recipe involved two stages. Stage one: supervised fine-tuning on approximately 6 million examples spanning math, code, science, tool use, and general chat. The data came primarily from NVIDIA's Nemotron-Post-Training-Dataset-v1 and AM-DeepSeek-R1-Distilled, both datasets containing synthetic reasoning traces generated by DeepSeek-R1-0528. Prime Intellect trained for one full epoch at 65,000 token context length using the Muon optimizer.
Stage two pushed to 98,000 token context length for agentic fine-tuning, then launched large-scale RL across environments spanning math (21,200 problems), code (8,600 problems), science (29,300 problems), and logic (11,600 problems). The training used GRPO with token-level importance sampling, running for approximately 600 steps with 256 prompts and 16 rollouts per prompt.
The Reddit community noticed immediately. One commenter on r/LocalLLaMA called it "basically a fine-tune" of GLM. Another estimated the continued pre-training alone cost $40,000 to $50,000, with the RL phase adding roughly $110,000. A third observed that Prime Intellect "is more focused on iterating training protocols for the open science community rather than the models themselves."
That last observation cuts to the heart of what Prime Intellect actually sells. The model demonstrates capability. The real product is the infrastructure that produced it.
Here's where the narrative gets complicated. Prime Intellect's origin story centers on decentralized training across globally distributed GPUs. INTELLECT-1, released in late 2024, trained a 10-billion-parameter model across five countries and three continents using up to 112 H100 GPUs simultaneously. The run achieved 83% compute utilization globally and 96% when nodes were confined to the United States. INTELLECT-2 pushed that to 32 billion parameters with reinforcement learning using permissionless compute contributors.
INTELLECT-3 abandoned that approach entirely for production. The technical report states the training ran on "512 NVIDIA H200 GPUs across 64 interconnected nodes" over two months. This is a traditional co-located cluster with 400Gbps NDR InfiniBand fabric (NVIDIA ConnectX-7 adapters), targeting at least 160 GB/s throughput. The team used Slurm with Cgroup v2 for orchestration, Lustre for high-throughput training I/O, and DCGM plus Prometheus for GPU monitoring. Standard hyperscaler infrastructure.
Run the numbers on compute. Discount cloud providers charge around $3.80 per H200-hour. Multiply that by 512 GPUs over two months and you're looking at $2.2 million in raw compute costs. Prime Intellect almost certainly negotiated better rates through reserved capacity, but even at half that figure, the training run consumed a significant chunk of the company's entire $20.5 million war chest.
So why abandon the decentralized approach that defined the company's pitch? The short answer: physics. When you're running asynchronous RL at this scale, policy weights need to reach inference workers mid-generation. If a rollout drifts more than eight steps behind the current policy, the team threw it out. That kind of tight synchronization demands InfiniBand, not commodity broadband from someone's garage.
Prime Sandboxes, the execution environment Prime Intellect built for this work, illustrates the engineering trade-offs. The system skips the Kubernetes API server entirely. Instead, a Rust gateway talks directly to pods through headless services. Spinning up a sandbox takes under ten seconds; running code happens in milliseconds. During INTELLECT-3 training, more than 4,000 of these sandboxes operated simultaneously.
The company has not hidden any of this. But the marketing emphasizes "decentralized AI" while the flagship model runs on precisely the kind of concentrated compute that the company was founded to challenge.
On AIME 2024, INTELLECT-3 hits 90.8%. The 2025 edition drops to 88.0%, still strong. LiveCodeBench v6 comes in at 69.3%, GPQA Diamond at 74.4%, MMLU-Pro at 81.9%. Every score beats what Z.ai's own post-trained GLM-4.5-Air manages.
But here's the thing. Z.ai also makes GLM-4.5, the bigger model with 355 billion total parameters. That one scores 85.8% on AIME 2024. And GLM-4.6, Z.ai's next release, reaches 92%. Prime Intellect's model lands somewhere in the middle of the family tree, performing well for its compute class without setting any records.
A more useful comparison is DeepSeek-R1-0528. That model scored 83.2% on AIME 2024 and 73.4% on the 2025 version. INTELLECT-3 clears both marks by comfortable margins. Given that the entire post-training budget ran somewhere around $150,000, the gap between Prime Intellect's numbers and DeepSeek's represents genuine value creation.
Prime Intellect claims benchmark scores "generally trend up and do not appear to have reached a plateau" during RL training. The implication is clear: more compute would yield more capability. This matches the broader pattern in reinforcement learning for language models, where performance scales with training steps until data diversity becomes the bottleneck.
Reading the technical report reveals Prime Intellect's actual business model. INTELLECT-3 serves as proof of concept for an infrastructure stack the company wants to sell.
prime-rl is a framework for asynchronous reinforcement learning that supports multi-node deployment with FSDP2 training and vLLM inference. The orchestrator handles continuous batching and in-flight weight updates. Prime Sandboxes execute untrusted code at scale, running over 4,000 concurrent containers during training with sub-second provisioning and millisecond execution latency. The Environments Hub hosts over 500 RL environments for training and evaluation.
The company plans to release "a hosted entrypoint to prime-rl as part of our upcoming Lab platform, enabling large-scale RL training without the infrastructure overhead." Translation: Prime Intellect wants to become the AWS of reinforcement learning.
This positions INTELLECT-3 less as a standalone product and more as a demonstration that the infrastructure works. The model shows what's possible with prime-rl, verifiers, and Prime Sandboxes. Companies licensing the platform could theoretically reproduce similar results on their own base models.
Distributed Global and CoinFund co-led Prime Intellect's $5.5 million seed round in April 2024. Ten months later, Founders Fund came in with $15 million for the Series A. Total raised: just over $20 million. The cap table features names that signal credibility in both AI research circles and crypto investing, including Andrej Karpathy, Hugging Face CEO Clem Delangue, SemiAnalysis founder Dylan Patel, FlashAttention creator Tri Dao, and crypto figures Balaji Srinivasan and Emad Mostaque.
The crypto angle matters. Prime Intellect operates a protocol on Base Sepolia testnet with smart contracts describing token-based payouts to compute contributors. The $15 million raise announcement mentions "a peer-to-peer protocol for compute and intelligence" that uses "crypto-economic primitives to commoditize compute and intelligence." The protocol includes TOPLOC, a lightweight verification scheme for checking rollouts from untrusted inference workers, and SHARDCAST for broadcasting policy weights across the network.
No token has launched yet. But the infrastructure exists for one. Blocmates and Binance Research have covered Prime Intellect as a DePIN (Decentralized Physical Infrastructure Network) project with similarities to Akash and io.net. The compute marketplace already integrates with Akash's permissionless GPU network.
The strategic implication: Prime Intellect may be building toward a tokenized compute network where contributors earn rewards for providing GPUs, data, or code. INTELLECT-3 demonstrates the training stack works. A native token could provide the economic layer that coordinates global participation. The company calls this "collective ownership of intelligence," a phrase that resonates differently in crypto circles than in traditional AI discourse.
Prime Intellect operates in a crowded field. On compute, the company competes with hyperscalers like AWS, GCP, and Azure, plus specialized GPU clouds like CoreWeave, Lambda, and RunPod. Its differentiator is aggregating multiple providers behind a single interface, including decentralized networks like Akash.
On decentralized AI training, competitors include io.net, Render Network, Gensyn, and Bittensor. These projects take different approaches: Bittensor uses a multi-subnet architecture with TAO token rewards, while Gensyn focuses on verifiable training computation. Prime Intellect's edge is shipping actual models and frameworks rather than just white papers.
On model development, Prime Intellect isn't trying to out-train OpenAI or Anthropic. The target is a different segment entirely: organizations that care about reproducibility, open weights, and the ability to participate in development. Hugging Face plays in this space. So do EleutherAI and Together AI.
And that positioning creates its own problems. Hobbyists find the infrastructure expensive. Enterprises want faster iteration and dedicated support. Companies seeking competitive moats wonder why they'd build on something their competitors can access for free. Prime Intellect occupies the uncomfortable middle where it's not cheap enough, fast enough, or proprietary enough for any single customer category.
Prime Intellect's trajectory reveals a recurring tension in AI development. The vision was decentralized training that democratizes access to frontier capability. The reality is that frontier RL requires centralized clusters, at least for now.
INTELLECT-1 and INTELLECT-2 proved distributed training works at moderate scale. INTELLECT-3 proved that centralized training produces better results when you need them. The company now straddles both approaches: decentralized compute marketplace for general workloads, centralized clusters for flagship models.
The technical contribution is real. prime-rl and the Environments Hub lower barriers for organizations that want to run RL post-training. The verifiers library standardizes how environments are built and shared. Prime Sandboxes solve the operational challenge of executing untrusted code at high throughput.
Whether this translates to commercial success depends on questions Prime Intellect cannot yet answer. Can the compute marketplace attract sufficient supply and demand to reach liquidity? Will enterprises adopt an open-source RL stack when cloud providers offer managed alternatives? Does the crypto-AI crossover appeal to a sustainable customer base, or does it signal to both camps that the company serves the other?
The model works. The infrastructure works. The business model remains to be proven.
For developers and researchers: INTELLECT-3 and prime-rl represent accessible entry points to large-scale RL training. The complete recipe is open-sourced, including model weights, training frameworks, datasets, environments, and evaluations. Teams can reproduce the results or adapt the approach for their own base models.
For the open-source AI ecosystem: Prime Intellect demonstrates that post-training on open base models can match or exceed proprietary alternatives. GLM-4.5-Air plus RL beats GLM-4.5-Air's own post-train from Z.ai. The implication is that compute and technique matter more than exclusive access to base models.
For decentralized compute networks: INTELLECT-3's centralized training highlights the gap between current decentralized infrastructure and frontier AI requirements. The market for distributed training at 10-32B parameters may be real. The market for distributed training at 100B+ parameters remains unproven.
Q: What is GLM-4.5-Air and why does it matter that INTELLECT-3 is based on it?
A: GLM-4.5-Air is a 106-billion-parameter foundation model from Chinese AI company Zhipu AI (Z.ai), pre-trained on 22 trillion tokens. Prime Intellect used it as their starting point rather than training from scratch. This means the heavy lifting, roughly 22 trillion tokens of pre-training, was already done. Prime Intellect added post-training on top, which cost around $150,000 versus the millions required for full pre-training.
Q: Can I run INTELLECT-3 locally on my own hardware?
A: Yes, but you need serious hardware. The model has 106 billion total parameters with 12 billion active during inference (Mixture-of-Experts architecture). Quantized versions (GGUFs) from providers like bartowski work on high-end consumer GPUs with 24GB+ VRAM, though some users report issues with long contexts causing endless thinking loops. Full precision requires multiple enterprise GPUs.
Q: What's the difference between INTELLECT-1, INTELLECT-2, and INTELLECT-3?
A: INTELLECT-1 (10B parameters) was trained across five countries on distributed GPUs, proving decentralized training works. INTELLECT-2 (32B) used distributed reinforcement learning with permissionless contributors. INTELLECT-3 (106B) switched to a centralized 512-GPU cluster for production quality. Each generation scaled up but moved away from the decentralized approach as model size increased.
Q: What is prime-rl and can anyone use it?
A: prime-rl is Prime Intellect's open-source framework for asynchronous reinforcement learning, available on GitHub. It handles multi-node training with FSDP2 and vLLM inference, plus in-flight weight updates during generation. Anyone can use it now, and Prime Intellect plans to launch a hosted version through their Lab platform so teams can run large-scale RL without managing their own GPU clusters.
Q: Is Prime Intellect launching a crypto token?
A: No token exists yet, but the infrastructure suggests one is coming. Prime Intellect runs a protocol on Base Sepolia testnet with smart contracts for compute contributor payouts. Their fundraising materials mention "crypto-economic primitives" and "collective ownership of intelligence." The compute marketplace already integrates with Akash's decentralized GPU network. No launch date has been announced.
Marcus Schuler
Maria Garcia
Robert Brown
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