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Over the past few years, we have witnessed the advent of large language models (LLMs), which have allowed us to "converse" with computers. Today, however, we are entering the phase of agentic AI. Unlike conventional models that merely generate text based on a query, agents can plan, use tools, correct their errors, and interact with other systems. It is this ability for autonomous action that creates unprecedented pressure on infrastructure, which current standards simply cannot handle.
Why are traditional data centers hitting a wall?
The main problem is not just the computational power itself (GPU), but the way data moves. Traditional systems were designed for sequential data reading – for example, when training models or streaming video. Agentic AI, however, works differently. Autonomous agents constantly "jump" between different information sources, verify facts in real-time, and require immediate access to vast datasets to verify context.
This process creates a so-called I/O bottleneck (input/output bottleneck). If the GPU has to wait for data to move from storage to memory, there is a drastic drop in efficiency. In this context, a solution emerges in the form of the NeuralMesh platform from WEKA. This architecture uses "zero-copy" technology, which connects GPU memory directly to NVMe storage across the entire cluster. According to available benchmarks, this can reduce agent response latency by up to 40%, which is a critical parameter for smooth human-AI interaction.
Efficiency: How to run thousands of agents on one machine
For businesses, the biggest challenge is cost. Purchasing proprietary clusters with state-of-the-art chips is extremely expensive. Here comes the solution from the partnership of Dell and Cognizant, which utilizes NVIDIA Fractional GPU technology.
Instead of one powerful chip (e.g., Vera Rubin architecture) serving only one task, virtualization allows it to be divided into dozens of "mini-compute instances". Each instance can be dedicated to one specific agent. This allows companies to scale their operations – from simple customer support to complex supply chain optimization – with much lower physical and financial costs. For a Czech company that wants to implement autonomous systems, this means they don't have to invest in huge server rooms but can utilize more efficient, virtualized models.
Intel Xeon 6: The Brain Behind the GPUs
While graphics cards (GPUs) perform the actual "thinking" and logical operations, we need someone to manage the entire process. This is the role of Intel Xeon 6 processors (Granite Rapids architecture). These processors act as "mission control" – an orchestrator that ensures agents don't interfere with each other and that network communication between them isn't overwhelmed.
Thanks to the integration of technologies like Advanced Matrix Extensions (AMX), Intel Xeon 6 can perform data pre-processing (e.g., security filtering or prompt formatting) directly on the CPU, without having to burden expensive GPU resources. This delegation of tasks is crucial for maintaining stability in systems where thousands of agents operate in real-time.
Practical Impact: What does this mean for the Czech market and the EU?
For the Czech technology scene and European businesses, this shift has two fundamental aspects:
- Costs and Availability: Implementing "AI factories" is capital-intensive. However, thanks to models like Dell/Cognizant, the barrier to entry is lowered. For Czech small and medium-sized businesses (SMBs), this means the opportunity to adopt agentic AI through cloud providers or specialized edge systems, without having to build their own data centers.
- Regulation and Security: Within the framework of the EU AI Act, transparency and oversight of autonomous systems will be crucial. The ability of processors (Intel) and platforms (WEKA) to perform "safety filtering" and ensure the auditability of agent decisions directly at the infrastructure level is a significant advantage for European companies in complying with strict AI safety rules.
If you are considering deploying AI in your company, don't just ask "which model will we use", but "what infrastructure will allow these agents to truly function without latency and in compliance with EU legislation".
What exactly is "agentic AI" compared to regular ChatGPT?
Regular AI (like standard ChatGPT) responds to your query and generates an answer. Agentic AI has the ability for autonomous planning: it receives a goal (e.g., "book me a flight and hotel according to budget") and independently chooses tools, searches the web, makes a payment, and then confirms the result to you.
Is building an AI factory realistic for a small Czech company?
Direct purchase of hardware like NVIDIA Vera Rubin is inaccessible for most companies. The realistic path is to use cloud services (Azure, AWS, Google Cloud) or local providers who already offer these "AI factory" components (virtualized GPUs, fast storage) as a service.
How much does the EU AI Act affect the operation of these systems?
Fundamentally. Autonomous agents fall into higher-risk categories. Companies must ensure that their infrastructure allows for logging agent decisions and that a "human-in-the-loop" mechanism exists (human in the decision-making process), which requires a specific management architecture.
