MythWorx Unveils TrainingWorx℠, a Modular Approach to Training Large-Language Models

AI solution introduces a category-defining method for training LLMs

DALLAS--(BUSINESS WIRE)--MythWorx, a pioneering AI product lab, today launched TrainingWorx℠, a more efficient, cost-effective approach to training large-language models (LLMs). The company also announced an upcoming, invite-only guided preview, giving select organizations early access to the platform.

Traditional AI forces enterprises into a difficult tradeoff: invest millions of dollars in infrastructure or fall behind. TrainingWorx℠ eliminates this constraint through a modular expert architecture that dramatically reduces cost while increasing flexibility, scalability and performance.

“TrainingWorx℠ enables enterprises to train advanced AI models on their own proprietary data without the cost, complexity and risk of traditional training,” said MythWorx CEO Jason Williamson. “TrainingWorx℠ is a foundational pillar of MythWorx’s broader mission to make enterprise AI more accessible, efficient and trustworthy.”

A novel approach to training LLMs

TrainingWorx℠ trains LLMs differently.

Traditional training approaches update billions of parameters simultaneously, using scale as a proxy for intelligence. This results in massive infrastructure costs, rigid architectures and systems prone to hallucination and knowledge degradation during updates.

Rather than treating models as monolithic systems trained end to end, TrainingWorx℠ uses a modular system of specialized experts that can be developed sequentially, aligned efficiently and deployed as a single, high-performance system.

This method reduces training costs by more than 90%, boosting the return on investment for AI/ML engineers and data scientists seeking to train domain-specific models without massive infrastructure.

Training LLMs with specialized experts

Specifically, TrainingWorx℠ trains one expert module at a time on domain-specific data using as few as 1 to 2 GPUs. These experts are then coordinated through a lightweight routing system during inference.

The approach avoids the “forgetting” that plagues traditional training approaches, as updates are isolated in individual experts. TrainingWorx℠ facilitates continuous improvement by allowing models to evolve periodically as new data arrives, with new domains added incrementally rather than via full retraining.

Such adaptability also helps future-proof AI architectures, as new capabilities may be added without full retraining.

Other features of TrainingWorx℠ architecture include full audit and reasoning chains, which are critical for regulated industries such as finance and healthcare, as well as the ability to run on cloud, on-premises, or at the edge with minimal hardware.

TrainingWorx℠ complements MythWorx’s broader capabilities in edge deployment, explainability, and low-power AI systems, enabling organizations to build and operate intelligent systems without reliance on hyperscaler infrastructure.

Guided preview program

To support the launch of TrainingWorx℠, MythWorx will open an invite-only preview for organizations.

Participants will be selected through an application process. The program will run for 20 days, starting on Nov. 4. Interested organizations can apply here: https://mythworx.ai/trainingworx-preview

Additional Resources:

• New Research Validates a Breakthrough in Behavioural AI
• Introducing NeuroWorx℠: AI reasoning at 2% of the power
• MythWorx Outperforms Hundreds of LLMs in MMLU-Pro Benchmarking

About MythWorx:

MythWorx is pioneering a new class of brain-inspired AI built on human-like reasoning, real-time learning and ultra-low compute. Its proprietary neuromorphic architecture enables systems that understand, adapt and self-improve without massive data, pretraining or energy-intensive infrastructure. Designed for mission-critical enterprise, government and defense applications, MythWorx delivers high-assurance, edge-ready, auditable intelligence that outperforms traditional AI while dramatically reducing cost, latency, hallucinations and power consumption. For more information, visit www.mythworx.ai.