Federated Core Training of Large Language Models

Federated Core Training of a 7B EURO STACK LLM Using SYNNQ Pulse

Objectives: This summary outlines the technical architecture and execution plan for the federated core training of a 7 billion-parameter foundational language model—the initial phase of the EURO STACK LLM—using SYNNQ Pulse, a distributed orchestration infrastructure designed for privacy-compliant, scalable AI training. This 7B model forms the baseline layer of a sovereign European LLM, trained exclusively on curated, audited, and legally compliant datasets, across a heterogeneous and decentralized network of compute nodes in Europe.

Training Framework: The SYNNQ Pulse system facilitates the training of large-scale models in environments where:

- Data cannot be centralized (due to legal or trust constraints),

- Compute is highly heterogeneous (ranging from HPC clusters to enterprise GPUs),

- Interoperability and fault tolerance are critical.

The goal is to distribute the training process across dozens or hundreds of compute nodes using federated orchestration, while ensuring:

- Training data integrity and version control; Hardware-aware workload scheduling; Secure training result integration.

Upon completion of the iterative training process, SYNNQ Pulse will release:

- A 7B parameter LLM checkpoint trained entirely on audited EU data, with verified provenance.

- Evaluation benchmarks and model cards detailing compliance, use cases, and known limitations.

- Inference APIs for initial use by stakeholders (e.g., government, healthcare, legal).

This model serves as the baseline foundation for larger follow-on models (24B, 70B, and beyond), reusing the same federated orchestration layer.

Conclusion

The core training of a 7B parameter foundational LLM using SYNNQ Pulse proves that federated, privacy-compliant AI training is not only possible, but scalable, secure, and efficient. By intelligently matching curated data with Europe’s diverse compute infrastructure, SYNNQ Pulse lays the foundation for a truly sovereign AI capability — built by Europe, for Europe.