Why Will 200 PFLOPS Change the Physics of Business in 2026?
SAVANT-AI is a research project in the inLABs programme. The capabilities described are research goals, not an available product. Project status: inLABs
In a global industrial corporation, every second of delay in accessing knowledge costs thousands of euros – and every additional millisecond of latency is a direct hit to EBITDA. This is not marketing rhetoric, but a consequence of a phenomenon that data physicists refer to as Data Gravity. The petabytes of data generated by MES, PLM, and ERP systems reach a critical mass at which attempting to transfer them to the public cloud generates transfer costs and delays that negate any benefits of using artificial intelligence. In the reality of industrial production, where the assembly line does not tolerate downtime, the distance between the source of information and the computing unit ceases to be a technical parameter – it becomes a determinant of the profitability of the entire enterprise. This is exactly where an on-premises sovereign cognitive infrastructure — like the one we research in the SAVANT-AI project — can turn the economics of AI from a “nice demo” into a “production-grade profit engine”.
Latency as a cost: the limits of cloud architecture
Traditional cloud architecture assumes that data can migrate freely to hyperscale computing centers. This assumption fails when confronted with the industrial reality of global conglomerates. When a maintenance engineer in a production plant needs immediate synthesis of a fact concerning a repair procedure for a critical machine, every millisecond of delay in translating the query through network layers translates into a real loss. Industry research indicates that highly skilled knowledge workers lose up to several percent of their working time searching unproductively through scattered information resources. On the scale of a global conglomerate, this amounts to tens of millions of euros per year – capital wasted due to an architecture that treats delay as an acceptable parameter. The answer to this systemic challenge is not another analytics platform or subscription-based cloud assistant. The answer is a fundamental paradigm shift: bringing computing power to where the data is generated.
Project assumption: compute next to the data
The NVIDIA HGX B300 (Blackwell Ultra) architecture delivers 200 PFLOPS of computing power in FP8 mode – performance that allows knowledge synthesis to be generated in milliseconds instead of seconds. The system has a total of 2.3 TB of VRAM (HBM3e), enabling it to handle models with 405 billion parameters at full numerical precision without degrading quantization quality. NVLink interprocess bandwidth reaches 1.8 TB/s, eliminating bottlenecks in multi-agent orchestration processes. This specification is not an abstract technical value – it translates directly into the ability of the Enterprise Cognitive System (ECS) to perform real-time inference processes. In practice, the SAVANT-AI project aims to handle thousands of concurrent, source-grounded queries from engineers, risk officers and procurement teams with short response times.
Permanent Model Residency: models kept in memory
The SAVANT-AI project assumes the Permanent Model Residency paradigm on the Blackwell architecture. By design, each unit — from the Llama-3 405B orchestrator, through seven 70B-class domain agents, to the deterministic fact-verification module — is meant to reside permanently in accelerator memory, with no “cold start” and no model swapping. Zero context switching latency means that a complex query requiring data correlation from ERP, PLM, and MES systems receives a response in milliseconds, not seconds or minutes as is typical for remote inference-based solutions.
Confidential Computing: sovereignty built into silicon
A critical aspect of the Blackwell architecture, often overlooked in discussions focused solely on computational performance, is the implementation of Confidential Computing technology. Inside the HGX B300 accelerators are Trusted Execution Environments (TEEs) – hardware enclaves in HBM3e memory, completely isolated from the operating system layer and processes that are not part of the certified cognitive core. Financial data, process recipes, pricing strategies, and unique technological parameters are processed in encrypted, impenetrable silicon capsules. In the SAVANT-AI project we research how to ensure sovereignty through hardware architecture, not only through contracts. Hardware-anchored sovereignty can make it easier to meet GDPR, NIS2 and DORA requirements in banking, energy and the public sector.
CapEx instead of a “success tax”
The economic model of cloud solutions is based on variable operating costs (OpEx), which increase proportionally to the scale of system adoption within an organization. This paradox can be described as a “success tax” – the more value an organization derives from AI, the higher the bill it receives from its cloud provider. Own infrastructure reverses this logic: a one-off capital expenditure (CapEx) replaces variable operating costs. The main expected benefit is a shorter Mean Time to Synthesis (MTTS) — the average time a knowledge worker needs to obtain a verified fact.
Deterministic fact verification and Deep Citation
A key goal of the SAVANT-AI project is deterministic fact verification (Factual Grounding) combined with Deep Citation: every claim is meant to be backed by source evidence. Each number in the decision interface is meant to come with an active link leading to a specific page in the PDF documentation, a record in the ERP system, or an entry in the PLM knowledge base. The Source Traceability mechanism is meant to make inference auditable, which is a prerequisite for compliance with ISO standards, industry regulations, and internal audit requirements. For audit and supervisory boards, this means that every AI‑assisted decision can be reconstructed, justified, and defended years after it was made.
Model Context Protocol and agent orchestration
The multi-agent architecture of the SAVANT-AI project is to be based on the Model Context Protocol (MCP) standard, which serves as a universal foundation for integration with corporate source systems. Instead of building costly connectors dedicated to each system-model pair, MCP introduces a client-server architecture where transactional systems share their resources through unified context servers. This can turn integration from a never-ending project into a standardised pattern. The main cognitive orchestrator (Llama‑3 405B) decomposes complex business questions into subtasks delegated to specialized domain agents responsible for extracting knowledge from specific data silos. The agents are meant to verify each other’s conclusions before aggregation, which should reduce the hallucination risk typical of monolithic generative systems.
Air-gap isolation as a digital firewall
The project assumes air-gap isolation that physically cuts the computing unit off from the public internet. This is a response to two fundamental threats: Advanced Persistent Threat (APT) attacks targeting strategic intellectual property and the risk of silent data seepage into the infrastructure of public cloud providers. Update management procedures are based on the Secure Gate mechanism using one-way data links (data diodes), which allows new model versions to be delivered securely without exposing the system to software supply chain compromise. This model reduces the risk of strategic data leaks. From the board’s perspective, air-gapped infrastructure acts as a digital firebreak: it limits the blast radius of a cyber incident by design, not only by policy.
The next step for CIOs and CFOs
Industrial organizations are currently facing an existential choice. Continuing with a strategy based on distributed cloud solutions leads to the perpetuation of AI Sprawl – technological chaos in which dozens of uncoordinated AI initiatives generate costs without building a lasting competitive advantage. The alternative is to consolidate around a sovereign cognitive infrastructure that transforms data into strategic knowledge where that data is created. The SAVANT-AI project assumes the NVIDIA Blackwell HGX B300 architecture and hardware-enforced sovereignty — a digital nervous system for the organisation rather than another IT tool.
For CIOs, CFOs and Chief Risk Officers, the next step is to assess which processes need AI close to the data. Available today as an on-premises deployment: SAIE