TrainingWorx℠

Enterprise AI at scale: A modern approach to cost-efficient model training.

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TrainingWorx℠ Datasheet

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TrainingWorx℠ Datasheet

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Get the full technical picture on TrainingWorx℠ — Architecture, cost comparisons, and training benchmarks, all in one datasheet.

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Enterprise AI at Scale

A breakthrough approach for enterprises seeking to unlock the full value of their internal data without incurring the prohibitive costs of traditional dense model training.

Add MythWorx when you need to run offline, prove every step of your reasoning, or bring your compute cost back under control — in mission-critical, off-grid, and highly regulated environments the cloud can’t reach.

Comparison Chart

Below is a comparison of traditional dense training versus TrainingWorx℠ across the dimensions that matter most to enterprises training a 7B model on 500 billion internal tokens.

Traditional Dense Training

7B Model, 500B Tokens

Peak Compute Requirement

Full 7B active per step. Requires multi-GPU high-memory nodes (A100/H100).

Memory Footprint

All parameters + optimizer states must fit in VRAM. High memory pressure.

Training Throughput

Full gradient updates across 7B parameters for all 500B tokens. Cluster required for speed.

Total Compute Cost

High. Compute scales with full 7B per token across 500B tokens.

Scaling Beyond 7B

Requires retraining or upgrading to 13B/34B/70B models.

Domain Specialization

Single FFN per layer must learn all domains; cross-domain interference.

Catastrophic Forgetting

Likely when training sequentially on large new datasets.

Update Frequency

Expensive. Requires large fine-tunes or partial retraining.

Failure Recovery

Single monolithic job. Restart required if failure occurs.

Inference Latency

Fixed cost for 7B FFNs per token.

Inference Capacity

Fixed at 7B performance.

Data Pipeline Fit

Designed for static, pre-built datasets.

Operational Complexity

Simpler architecture. Complex cluster orchestration.

Long-Term Scalability

Scaling requires building bigger dense models.

TrainingWorx℠ Modular Training

7B Trunk + Experts

Peak Compute Requirement

Only trunk + one expert active. Fits on 1–2 GPUs with CPU/NVMe offloading.

Memory Footprint

Parameters, grads, optimizer states sharded across CPU/NVMe. Small active footprint.

Training Throughput

Each expert trained on a subset of the 500B tokens. Sequential jobs. Lower cost.

Total Compute Cost

Lower. Compute scales with expert size (50–150M) + trunk, not full model.

Scaling Beyond 7B

Add more experts to expand overall capacity without new dense model.

Domain Specialization

Experts can specialize per domain. Minimal interference.

Catastrophic Forgetting

Very low. Earlier experts remain untouched.

Update Frequency

Frequent updates possible. Train new experts cheaply and integrate.

Failure Recovery

Only re-run the affected expert. Training is modular.

Inference Latency

Near 7B latency. Only top-k experts activated.

Inference Capacity

Behaves like a 40B–200B model due to expert library.

Data Pipeline Fit

Optimized for streaming data (Kafka/Flink) and continuous ingestion.

Operational Complexity

Modular architecture. Simpler hardware footprint, staged jobs.

Long-Term Scalability

Add experts indefinitely. Lifelong learning path.

You already built the foundation. Let's help it go further.

Whether it's a factory floor, a ship, a hospital, or an air-gapped facility — if your AI needs to work where the cloud can't follow, we should talk.

info@mythworx.ai

Products Support

support@mythworx.ai

You already built the foundation. Let’s help it go further.

Whether it’s a factory floor, a ship, a hospital, or an air-gapped facility — if your AI needs to work where the cloud can’t follow, we should talk.

info@mythworx.ai

Products Support

support@mythworx.ai