In keeping with our Environmental commitments and our Mission as an ethically driven business, we create and optimize the models, algorithms, data and infrastructures that each use case actually requires. Specialized architectures and HPC-grade engineering deliver state-of-the-art performance without the waste generated by unnecessarily large, general-purpose AI systems.
This approach reduces computing, storage and network requirements throughout our solutions lifecycle, from training to everyday inference. Depending on the use case, GenerIA technologies can operate on CPUs, edge devices or mobile hardware, consume up to 90% less energy and achieve audited and certified carbon neutrality.
Frugality is not reduced ambition, it is better engineering: more useful performance, lower operating costs and the smallest possible environmental footprint.
The largest possible AI model is rarely the most effective answer to a clearly defined business need. Larger systems require more memory, computing power, electricity and cooling, while often delivering little additional value for specialized tasks.
Right-sized for every use case - GenerIA selects the model architecture and capacity according to the complexity, knowledge domain, latency requirements and deployment environment of each application.
Specialized instead of oversized - Carefully selected data and domain specialization allow smaller systems to deliver highly relevant results without reproducing the cost and environmental footprint of a universal, large-scale model.
Measured by useful results - Frugality is evaluated through the relationship between functional performance and the resources required to achieve it. A smaller model is not sensibly frugal if it produces unusable answers; an efficient system must preserve or improve the required quality of service.
Environmental impact does not begin and end with model inference. It includes data collection, preparation, training, storage, deployment, networking, monitoring and eventual retraining. GenerIA therefore approaches frugality as a system-wide engineering discipline, taking a holistic view and applying optimizations across every layer.
Less unnecessary data - Relevant, representative and well-governed datasets reduce training and processing requirements while improving domain accuracy.
Less unnecessary computation - Model sizing, optimized algorithms and targeted pipelines avoid using high-cost generation where retrieval, deterministic processing or a smaller specialized model, including a Machine learning model instead of a generative model, is sufficient.
Less unnecessary movement and storage - Direct access to existing data stores, optimized data locality, controlled indexing and lower data transfer volumes reduce duplication, network traffic and additional persistence requirements.
Better use of existing hardware - Depending on the application, GenerIA models can operate on CPUs, edge devices or mobile hardware, extending the useful life of existing equipment and reducing dependence on permanently active GPU infrastructure. We also encourage the use of repurposed hardware (including CPUs, GPUs and memory) wherever appropriate, while always using new storage devices to safeguard reliability and data integrity.
Strategic partnerships with infrastructure specialists - Close partnerships with data center builders and private cloud architects enable us to deliver on our commitments to end-to-end frugality (and sovereignty). We regularly go to market together as a consortium, with offerings that embed these principles by design.
Reducing resource requirements also reduces infrastructure, energy and operating costs. Smaller deployment footprints make professional AI accessible to more organizations and easier to operate in local, sovereign or disconnected environments.
Frugality therefore reinforces several other GenerIA characteristics at once: sovereignty, flexibility, affordability and resilience.
Our methodology follows the principles and best practices of AFNOR Spec 2314, to which GenerIA contributed. These practices address the entire AI lifecycle, from the initial assessment of a project's actual usefulness to continuous measurement after deployment.
Needs and utility analysis - Before designing a model, we identify the task, expected value, quality thresholds and available technical resources. We also determine whether every stage genuinely requires AI. Avoiding an unnecessary model call is the most efficient inference possible.
Targeted data engineering - Data is selected for relevance, diversity and quality rather than accumulated indiscriminately. Cleaning, deduplication, normalization and metadata enrichment increase the useful information obtained from every stored and processed unit.
Model rationalization - We benchmark candidate architectures against the use case and select the best performance-to-resource ratio. Depending on the requirements, a workflow may combine several smaller specialized models, including machine-learning models or generative Mixture of Experts (MOE) architectures, instead of relying on a single oversized system.
Compression and numerical optimization - Where appropriate, techniques such as pruning and quantization reduce model size, memory pressure and computation requirements while preserving the required level of accuracy.
Efficient retrieval and context construction - Retrieval pipelines select only the information needed for the current request. Reducing irrelevant context lowers token processing, latency and inference energy while generally improving response precision.
HPC-grade software optimization - Advanced profiling identifies CPU, GPU, memory, storage and network bottlenecks. Efficient implementations, concurrency control, batching, caching and hardware-aware execution reduce the amount of infrastructure required to meet performance targets.
Infrastructure-aware deployment - Hardware efficiency, expected utilization, data-center PUE, electricity sources, cooling requirements and network distance are considered when selecting a deployment environment. The same model can have very different impacts depending on where and how it runs.
Continuous metrology - Functional quality, latency, computing activity and energy use are measured in production. Alternative configurations can be compared through controlled testing, allowing the system to evolve toward a better performance-to-resource ratio.
Measured carbon neutrality - Once consumption and associated emissions have been minimized and quantified, the remaining footprint can be incorporated into an auditable and independently certifiable carbon-neutrality process. Carbon neutrality is therefore the final stage of frugal engineering, not a substitute for it.