An AI training and deployment platform that enables businesses to retain ownership of their AI models.
Eliminating reliance on external services, your business can deploy large language models (LLMs) such as LLaMA and ChatGLM on your own servers, and train customized digital tools—including customer service bots and medical assistants. End-to-end on-premises operation ensures data never leaves your premises, fully aligning with compliance requirements.
Our platform features an intuitive graphical interface, allowing both IT staff and non-technical users to easily handle model installation, data uploads, and model fine-tuning—no complex command-line operations required. It also supports training methods like RAG and LoRA, and comes with pre-built modules for various industries, empowering businesses to rapidly implement AI solutions.
Infrastructure capabilitiesThe case for keeping models, data and infrastructure inside your own perimeter.
Self-hosted models dramatically reduce recurring API costs, delivering up to 70% savings over time.
All data remains on your infrastructure, providing enterprise-grade protection for sensitive information.
Integrate proprietary documents and domain expertise to build models aligned with your organization's unique knowledge base.
Leverage multiple fine-tuning approaches and application modules for seamless deployment across diverse use cases.
Deploy both platform and models entirely within your infrastructure, eliminating dependencies on third-party services.
Enterprise-grade optimization delivering millisecond-level response times for superior user experiences.
Server plans below are shipping configurations with published specifications. Platform coverage beyond H200 is in qualification.
| Specification | 32B Module PlanSME development | 70B Module PlanEnterprise training | 671B Module PlanLLM research |
|---|---|---|---|
| Case size | 4U | 4U | 4U×2 |
| Memory | 48G GDDR6X | 192G GDDR6X | 768G GDDR6X |
| AI performance | 1,320 TOPS | 2,640 TOPS | 10,560 TOPS |
| Processor | 2 × 32 core | 2 × 32 core | 4 × 32 core |
| GPU configuration | 2 × NVIDIA Tesla H200 | 4 × NVIDIA Tesla H200 | 8 × NVIDIA Tesla H200 |
| Network interface | Dual 25G | Dual 25G | Dual 25G ×2 |
| Intended workload | Ideal for SME AI model development and testing, supporting multiple open-source model training and fine-tuning workflows. | Enterprise-grade AI training platform supporting large-scale model training and seamless commercial application deployment. | Top-tier AI training server optimized for large language model research and enterprise-grade AI applications. |
| Platform | Classpositioning | Statusavailability | Published configuration |
|---|---|---|---|
| NVIDIA H200 | Training / inference | Shipping in the plans above | 2 / 4 / 8 GPU |
| NVIDIA H100 | Training | In qualification | On request |
| NVIDIA A100 | Training / HPC | In qualification | On request |
| RTX PRO 6000 SE / H / B | Workstation / inference | In qualification | On request |
| RTX 5090 | Workstation | In qualification | On request |
The four things a GPU deployment is actually judged on, plus the topology they sit in.
Optimized data center environment engineered for peak cooling efficiency and consistent performance.
Enterprise-ready port ecosystem supporting diverse application requirements.
Comprehensive system analytics ensuring optimal operational efficiency.
Dual 25G uplinks, 10GbE host network, SATA/SAS storage expansion.
Utilization, system health and container orchestration in one operational view — the same telemetry that backs the SLA.
Utilization, thermal state, network throughput and storage I/O are tracked per node; container orchestration status is reported alongside.
Four programmes that turn GPU capacity into an asset class — tokenized compute, a marketplace, real-world assets and decentralized infrastructure.
Tokenized AI compute — how capacity is issued, held and redeemed.
A marketplace for GPU capacity: trading, matching and leasing.
Tokenization of physical assets — GPUs, racks and compute capacity.
Decentralized AI infrastructure — node architecture and collaboration models.
Six sectors and the workloads they run.
Automate legal document analysis, contract review, and case research to save time and reduce costs.
Personalize learning experiences, automate grading, and provide intelligent tutoring for students.
Enhance diagnostic accuracy, predict patient outcomes, and optimize treatment plans with AI.
Improve customer experience, optimize pricing strategies, and personalize product recommendations.
Analyze customer behavior, automate content creation, and optimize marketing campaign performance.
Predict equipment failures, optimize production schedules, and improve quality control processes.
Frameworks, models and schedulers supported by the platform.
Tell us the workload and the deployment constraints; we come back with a configuration and a delivery schedule.