Every training run, every cluster, one platform.
End-to-end training and fine-tuning infrastructure for enterprise AI teams. Maxis manages distributed workloads, GPU and NPU resource allocation, experiment tracking, and pipeline orchestration — so your team focuses on building better models, not the infrastructure beneath them.
Development, training, and evaluation — connected in one place.
Maxis brings Kueue scheduling, workload execution, and development, training, and evaluation together on one platform. Refine models with proven job templates and one-click fine-tuning, and divide GPUs across projects through queues and priorities. Pipelines and versioning let you trace which settings produced which results. Models built in Maxis go straight to serving on Metis.
From data ingestion to fine-tuned model endpoint, end-to-end
Run Maxis inside the environments your enterprise controls.
The same training platform — identical pipeline orchestration, experiment tracking, and resource scheduling — across your own data center and fully air-gapped environments.
On-Prem Private Cloud
Run Maxis entirely within your own data center, including fully air-gapped environments. Training executes on your own GPU and NPU hardware, on your data, within your security perimeter.
From experiment to production model — a complete training lifecycle.
Development & Fine-Tuning
Training and fine-tuning in one environment
Notebook-based model development, training runs, and fine-tuning in a single integrated environment. Configure, launch, and monitor training jobs without leaving the platform or context-switching between tools.
Workload Execution
Standardized workload execution
Execute training and batch workloads via reproducible templates and volume-based configuration. Every run is versioned, reproducible, and auditable — eliminating the "it worked on my machine" problem at enterprise scale.
Resource Scheduling
AI infrastructure orchestration
Intelligently schedule GPU and NPU resources across clusters and workloads. Priority-based queue management, retry-and-backoff logic, and utilization-aware placement maximize hardware efficiency across concurrent training jobs.
Experiment Tracking
Automatic logging for every run
Hyperparameters, metrics, artifacts, and hardware utilization are automatically logged for every training run. Compare experiments, reproduce any result, and build confidently on prior work — without manual record-keeping.
Distributed Training Pipelines
Scale from fine-tuning to pretraining
Multi-node, multi-GPU and NPU training configurations managed natively. Scale from single-machine fine-tuning to large-scale distributed pretraining without infrastructure rework or custom parallelism configuration.
Metis Integration
Direct path from training to production serving
Models trained and fine-tuned in Maxis deploy as Metis inference endpoints — a single governed path from training to production inference, with no manual export or format conversion.
How Maxis fits into the stack.
Maxis is the training layer that produces the models Metis serves.
Maxis
The AI training and fine-tuning platform. Manages the full lifecycle from experiment configuration to reproducible model production — across GPU and NPU hardware, distributed or single-node.
When to use Maxis.
When you need to adapt an open model to your own data
Start from a model you have already validated and fine-tune it on in-house data.
When several teams need to share GPUs
Queues and priorities set the order, and each project gets its share.
When you need to trace which settings produced which results
Pipelines and versioning let you reproduce every run.
When training data cannot leave the company
Your data and your models stay inside your own environment.