EcosAI

The EcosAI Ecosystem

The EcosAI ecosystem is a modular hardware-software infrastructure that enables organizations to design, build, and operationally manage their own AI solutions – autonomous environments for developing, training, validating, and deploying artificial intelligence systems.

EcosAI is an infrastructural foundation for organizations/teams that:

• require full control over their AI data and processes. Through on-premises and hybrid deployments, EcosAI minimizes dependency on external cloud providers, enabling protection of intellectual property and sensitive data, as well as compliance with applicable legal regulations;
• want to own, control, and scale their resources instead of relying solely on external cloud services;
• treat AI as a strategic asset and want to develop it within their own technological ecosystem, with full control over data, costs, and the direction of development.

EcosAI is a solution consisting of two configured and optimized environments:

Software Environment

• a pre-installed and optimized Ubuntu Server system;
• a full set of deep learning frameworks: PyTorch, TensorFlow, Keras;
• customized and optimized NVIDIA GPU drivers;
• NVIDIA Toolkit and NVIDIA Container Toolkit enabling AI workloads to run in containerized environments;
• a self-hosted automation platform used to orchestrate research, experimental, and educational processes;
• a set of tools supporting full lifecycle management of machine learning projects – from experimentation, through model tracking, to deployment.

Hardware Environment

The use of the latest AMD processors and professional NVIDIA graphics cards, combined with a ready-made software environment, guarantees uninterrupted performance even for the most demanding tasks. Whether you’re working on data analysis, video rendering, or advanced scientific research – our solutions let you focus on results, not hardware limitations.

In the world of modern analytics, deep learning, and advanced scientific computing, powerful hardware is the foundation of success. Our workstations have been designed for maximum efficiency in applications requiring high computing power, offering the best combination of AMD processors and NVIDIA graphics cards.

Depending on requirements and intended use, EcosAI is delivered with one of the specially tailored and parameterized hardware environments from the HES (High End System) line:

HES-Tuna – Accessible Performance

An attractively priced solution using a single GPU. Excellent GPU performance combined with substantial CPU computing power, enclosed in a compact case. Approximate specifications:

• up to 8 compute cores (16 threads),
• 32 to 128GB of DDR5 RAM,
• up to 16TB PCIe NVMe SSD,
• GPU: 1x (RTX PRO 4500 Blackwell 32GB, RTX PRO 4000 Blackwell 24GB, RTX PRO 2000 Blackwell 16GB).

HES-Sailfish – High Compute Power in a Small Footprint

A high-performance workstation equipped with a single GPU. A multi-core AMD Ryzen processor together with an RTX PRO 6000 Blackwell Max-Q 96GB card in one case delivers outstanding performance for AI processes. Approximate specifications:

• up to 16 compute cores (32 threads),
• 32 to 256GB of DDR5 RAM,
• up to 24TB PCIe NVMe SSD,
• GPU: 1x (RTX PRO 6000 Blackwell Max-Q 96GB, RTX PRO 5000 Blackwell 48-72GB, RTX PRO 4500 Blackwell 32GB, RTX PRO 4000 Blackwell 24GB, RTX PRO 2000 Blackwell 16GB).

HES-Swordfish – Impressive Performance on Demand

A high-performance workstation dedicated to working with EcosAI. Support for two GPUs delivers exceptional performance available at any moment. A wide range of configurations allows the machine to be parameterized for AI applications defined by the client. Approximate specifications:

• up to 16 compute cores (32 threads),
• 32 to 256GB of DDR5 RAM,
• up to 32TB PCIe 5.0 NVMe SSD,
• GPU: 2x (RTX PRO 6000 Blackwell Max-Q 96GB, RTX PRO 5000 Blackwell 48-72GB, RTX PRO 4500 Blackwell 32GB, RTX PRO 4000 Blackwell 24GB, RTX PRO 2000 Blackwell 16GB).

HES-Marlin – No Compromises

A hardware environment delivering uncompromising computing power. A multi-core AMD Threadripper processor, multi-channel ECC RDIMM memory support, and support for four RTX PRO 6000 Blackwell Max-Q 96GB cards guarantee ultimate performance in any application. Full configuration freedom. No limitations. Pure power.

• up to 96 compute cores (192 threads),
• up to 1024GB of DDR5 RDIMM ECC RAM,
• up to 32TB PCIe 5.0 NVMe SSD,
• GPU: 4x (RTX PRO 6000 Blackwell Max-Q 96GB, RTX PRO 5000 Blackwell 48-72GB, RTX PRO 4500 Blackwell 32GB, RTX PRO 4000 Blackwell 24GB, RTX PRO 2000 Blackwell 16GB).

Also available in a 19″ RACK version.

HES-Orca – Multiprocessor Server

A custom-configured solution built to order.

EcosAI provides a solid foundation for quickly launching your own AI projects and experimenting with new solutions without the need to configure the environment yourself. EcosAI supports organizations in:

• building sovereign AI competencies,
• monetizing their own models,
• creating a lasting competitive advantage based on AI.

EcosAI – Security and Data Privacy

EcosAI enables the construction of private and hybrid AI solutions in which all key resources remain under the organization’s direct control.

The organization retains full control over:

• computing infrastructure and storage,
• AI models and their lifecycle,
• training, experimental, and production data,
• security, access, and audit policies.

The EcosAI architecture supports:

• separation of environments (research, education, production),
• isolation of data and workloads,
• enforcement of access policies compliant with organizational and regulatory requirements,
• secure deployment and operation of AI models in sensitive environments.

EcosAI – Key Benefits

Reduced Entry Barriers and Operating Costs

Using the AIQLAB PL EcosAI ecosystem significantly simplifies the process of building and maintaining local AI environments. The platform eliminates the need for manual configuration of infrastructure, tools, and dependencies, leading to:

• shorter AI environment deployment times,
• reduced operational and administrative costs,
• lower risk of configuration errors,
• standardization of environments across the organization.

Letting Teams Focus on Substantive Work

Thanks to the preconfigured and automated EcosAI architecture, users – engineers, researchers, R&D teams, and students – can focus directly on the highest-value activities, such as:

• designing and developing AI models,
• analyzing and interpreting experimental data,
• optimizing algorithms and computational processes,
• conceptual and testing work.

Competency Development and Implementation Support

EcosAI enables access to dedicated training services in the areas of:

• designing and implementing Agentic AI solutions,
• working with autonomous AI systems,
• effectively utilizing AI solutions in research, educational, and industrial environments.

The training supports organizations in building internal competencies and accelerates the adoption of advanced AI solutions.

A Foundation for Further Automation and Autonomy

EcosAI serves as a base for the next stages of an organization’s development in the field of AI, enabling:

• integration of Agentic AI,
• execution of autonomous experiments,
• building educational systems based on real computing and laboratory environments,
• gradual transition from experimental environments to autonomous AI Factories.

EcosAI – Example Applications

• AI for Quantum,
• AI for Science – supporting scientific research, simulations, and experiments,
• AI for Education – educational environments and teaching laboratories,
• AI for Industry – developing and deploying models that support business processes,
• Agentic AI – building and operationalizing autonomous systems.

Video Rendering and Post-Production

Render high-resolution projects faster than ever. Thanks to multi-core AMD processors and NVIDIA GPU acceleration, video export times are significantly reduced, allowing creators to focus on creativity instead of waiting for renders to finish.

Scientific Computing and HPC

Our solutions are ideal for tasks requiring simulation, modeling, or analysis in fields such as physics, chemistry, and engineering. High-performance AMD CPUs and powerful NVIDIA GPUs accelerate parallel computations, enabling complex scientific projects to be completed in record time.

Deep Learning and AI

Train complex AI models on your own data locally, without compromising on performance. Our workstations support both training large neural networks and real-time inference, ensuring maximum efficiency for data scientists and AI engineers.

Building Your Own Local Business Solutions

• An environment for building Quantum.ai – a quantum technology laboratory using AI techniques for simulation, quantum modeling, and calibration of physical ion-trap systems,
• tools for scheduling IT projects.

EcosAI – Functional and Architectural Scope

EcosAI covers the full AI lifecycle:

• data and data source management,
• model development and training (including Deep Learning),
• validation and experimentation,
• production deployment,
• support for research, educational, and operational modes.

EcosAI functions as an automation and orchestration layer, enabling:

• creation of development environments for Agentic AI,
• running AI-assisted experimental laboratories,
• integration of simulation and laboratory computations,
• secure deployment of models in production environments.

EcosAI – Deployment Model and Control

EcosAI supports:

• on-premises deployments,
• hybrid architectures,
• gradual scaling of computing power (GPU workstation / HPC workstation / edge server).

The entire system has been designed with:

• protection of IP and sensitive data,
• regulatory compliance,
• separation of environments (research, education, production) in mind.

EcosAI – Modular Architecture and Phased Development

EcosAI enables you to build an AI solution step by step:

• pilot and experimental environments,
• scaling of computing power and teams,
• automation of AI processes,
• achieving operational autonomy.

The modular architecture allows the platform to be adapted to:

• the organization’s current needs,
• the pace of AI adoption,
• evolving technological and regulatory requirements.