Last Updated on January 3, 2026
Introduction
For those following the evolution of decentralized AI training, Intellect-2-27B represents a significant milestone in distributed compute efficiency. Emerging from the research into how large-scale models can be trained across geographically dispersed sites, this model offers a unique opportunity to test the capabilities of high-parameter “Reasoner” models in a local environment. This exploration of specialized model performance is an inclusive part of our comprehensive guide to Private AI for Word, where we prioritize the shift toward 100% data ownership.
As part of our testing to bring diverse local LLMs into Microsoft Word via GPTLocalhost, we have evaluated the Intellect-2 model. This pioneering 32B model is uniquely trained via globally distributed reinforcement learning and we are curious about how this model performs for creative and technical writing.
Watch: Intellect-2 Performance Demo
This demonstration illustrates the integration of Intellect-2 within Microsoft Word. The video shows how the model handles long-form creative drafting, from initial brainstorming to structured narrative reasoning and writing.
Our demo video demonstrates how seamless and efficient this process can be. For more creative ideas on using private GPT models in Microsoft Word, please visit the additional demos available on our @GPTLocalhost channel.
Technical Profile: Why Intellect-2? (Download Size: 19.85 GB)
Intellect-2 is a 32B parameter model that serves as a landmark proof-of-concept for decentralized AI development. Unlike models built in massive, power-hungry centralized data centers, Intellect-2 was refined through globally distributed reinforcement learning (RL) using the GRPO-based training technique. By pooling heterogeneous compute resources from around the world, this model demonstrates that high-level “reasoning” capabilities can be matured outside of traditional corporate clusters.
- Inference-Heavy Training: Shifting from traditional pre-training, Intellect-2 utilized a 1:4 training-to-inference compute ratio, spending significantly more resources on generating and verifying “thought samples” to ensure logical consistency in complex drafting.
- Scalable Reasoning Architecture: Built upon the QwQ-32B base, it utilizes verifiable rewards to improve performance in math and coding, proving that RL-tuning of 32B models is now technologically viable across a distributed network.
Ultimately, Intellect-2 represents a new potential direction toward AGI. By moving away from the requirement for single, multi-gigawatt data centers and instead utilizing distributed, spare compute capacity, this architecture provides a blueprint for scaling intelligence while significantly spreading the environmental and power consumption bottlenecks of traditional AI development.
Deployment Reminders: Running Intellect-2 Locally
Our primary testing was conducted on an M1 Max (64 GB). For those wishing to contribute to the inference or training network (which is how the model was developed), specific VRAM is necessary depending on the role:
- Running the Model for Inference (locally): If you intend to run the pre-trained Intellect-2 model yourself, the necessary VRAM depends on the model quantization level (GGUF format) you choose to download.
- To run the model at maximum speed entirely on GPU VRAM, you need a GPU with VRAM capacity slightly larger than the model file size (e.g., a 24GB GPU can run many quantized versions, but might run out of memory for a full 13B parameter version in certain contexts).
- Inference Workers: A machine equipped with 4× NVIDIA RTX 3090 GPUs (each typically having 24GB of VRAM) was cited as a sufficient example for contributing to the 32B-parameter model training run. The model uses sharding techniques (like PyTorch FSDP2) to distribute weights across available GPUs.
The Local Advantage
Running Intellect-2 locally via GPTLocalhost ensures:
- Data Ownership: No cloud data leaks.
- Zero Network Latency: Faster performance on GPU and Apple Silicon.
- Offline Access: Work anywhere, including on a plane ✈️, without an internet connection.