TNG Technology Consulting GmbH announced a major milestone on December 7, 2025: its R1T2 Chimera model has surpassed one trillion tokens processed since its July release.
Behind this headline sits the real story—the rise of Chutes AI, the decentralized serverless compute network powering R1T2 and increasingly becoming the backbone for large-scale inference across the industry.
R1T2 Chimera: A Leap in Efficient, High-Throughput Model Design
TNG Technology—“The Nerd Group”—is a Munich-based engineering firm with 900+ specialists, more than half PhDs. Its R1T2 Chimera builds on the earlier R1T model, which handled half a trillion tokens earlier this year.
Constructed via direct tensor-level edits, R1T2 fuses three DeepSeek models into a single “TriMind” architecture.
Key performance highlights:
- 200% faster inference than DeepSeek R1-0528
- ~90% of the intelligence retained, validated by independent benchmarks
- 933B input tokens + 84B output tokens processed since launch
- Average latency of 2.57 seconds in production
- Fully open-source and hosted on Hugging Face
This performance profile explains why R1T2 is used across everything from chat systems to high-volume analytical workloads.
Chutes AI: The Decentralized Compute Engine Behind the Milestone
The engine behind R1T2’s trillion-token surge is Chutes AI, a decentralized, distributed, serverless compute layer built for high-throughput inference.
Running on Bittensor’s subnet 64 (SN64), Chutes combines a global GPU mining network with a serverless developer experience:
- Instant-on inference—no managing servers, clusters, or autoscaling
- Up to 85% cheaper than traditional cloud inference platforms
- Meritocratic rewards: miners earn based on performance
- Supports all major open-source models (DeepSeek, Qwen, Mistral, GLM, and more)
- Handles multimodal workloads including LLMs, embeddings, image/video generation, moderation, and 3D tasks
- Powered by the VLLM engine, enabling high-efficiency memory use and PagedAttention
- 99.9% uptime, global load balancing, cold-start optimization
- Python SDK, custom model deployment, full autoscaling
- Free tier + up to $20,000 in startup credits
Most importantly, no idle-time cost, which is a major contrast with centralized clouds.
The Bigger Picture: A New Compute Paradigm
TNG’s trillion-token R1T2 achievement proves a larger point:
High-scale inference no longer requires centralized mega-clouds. Decentralized networks like Chutes are now competitive at industrial scale—faster, cheaper, and more open.
This shift arrives as the global AI market heads toward $1.8 trillion by 2030, with inference forming the majority of that spend.
Chutes’ model aligns perfectly with where the market is going:
- Usage-based billing
- Developer-first tooling
- Permissionless participation
- Global GPU liquidity
- Near-zero infrastructure overhead
As adoption climbs into trillions of tokens per month, Chutes is on track to become the default inference layer for a decentralized AI future.
Conclusion
TNG’s R1T2 hitting one trillion tokens is more than a technical success. It highlights the strength of an entire compute paradigm. Chutes AI has demonstrated that decentralized, serverless GPU networks can deliver scale, cost efficiency, and reliability that match or exceed centralized clouds.
With millions of users, trillions of tokens processed, and rapidly expanding enterprise adoption, Chutes is positioning itself as the backbone of next-generation AI workloads.
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