Invergent Analysis: How We Tested the New DenseMax Server in Real Business Scenarios and What We Discovered About Hidden Costs and Data Sovereignty Risks in the Cloud Era
Author: Invergent.ai Labs
After nearly two decades of cloud dominance, we’ve become used to paying for computing power by the megabyte or by the hour. As generative AI becomes ubiquitous, the whole cycle seems to be repeating. Although AI costs may seem relatively affordable now, this is largely due to massive subsidies and speculative market valuations. Companies offering foundation models as a service (MaaS) are currently selling token access below real operating cost. Profits will eventually have to come either directly from customers’ pockets or through the exploitation of their data.
At Invergent, we observed this growing tension among our clients: an increasing dependence on powerful AI tools, coupled with proportional anxiety about unpredictable costs and data confidentiality. This challenge pushed us to develop our own hardware solution: DenseMax, a local enterprise AI server, optimized and designed to provide companies with the power of generative AI at a predictable cost and with guaranteed data sovereignty.
To validate our approach, we decided to put DenseMax through a rigorous test. We replicated specific client workloads, running them in parallel on our local server and on major cloud AI platforms. Our findings show an imminent paradigm shift.
Scenario 1: The Privacy Stress Test in the Legal and Financial Sectors
The first challenge came from a sector where confidentiality is non-negotiable. A law firm we work with analyzes thousands of pages of contracts and litigation documents—a perfect task for RAG (Retrieval-Augmented Generation). Their main concern: the risk that sensitive client data might be exposed or used to train future cloud provider models.
This distrust is widespread. A recent Pew Research Center study found that 81% of Americans are concerned that AI firms will misuse their data. Even though OpenAI claims it will delete conversations upon request, the reality is more complex. A recent court order in the New York Times lawsuit forced the company to retain chat logs. Moreover, Anthropic recently extended its data retention policies from 30 days to five years, moving to an opt-out model for user data training.
In our test, we configured the local DenseMax server with an open-weights model specialized in legal language. The results were clear: while the cloud model delivered comparable answer quality, the local solution completely eliminated the risk of data exposure. For European companies operating under GDPR, like the German company Makandra which built its own local AI to ensure compliance, physical control of hardware is not a luxury—it’s a strategic necessity.
Ovidiu Oancea, CEO of Invergent:
“We created DenseMax starting from a simple business reality: AI shouldn’t be a resource you rent in fear from Silicon Valley tech giants. It should be a strategic asset you own and control. For clients in regulated industries, the difference between a local server and the cloud is the difference between full compliance and systemic risk.”
The second test targeted R&D and software development departments. Here, the main problem is the exponential cost of experimentation. Developers using cloud APIs for intensive tasks such as code generation or real-time data analysis frequently run into usage limits (rate limiting). Yagil Burowski, founder of LM Studio, captured the frustration perfectly:
“It was a real obstacle to constantly remember that every time I ran my code, it cost money—because there was so much to explore.”
We simulated a week-long development sprint, with continuous tasks of code generation and debugging. In the cloud, token-based costs quickly escalated, surpassing the break-even point for experimental projects. Moreover, we encountered speed limitations during peak hours.
Running the same tasks on DenseMax, the marginal cost per query dropped to zero (after amortizing the initial hardware investment). This unlocked a level of productivity that the pay-as-you-go model actively discourages.
Flavius Burca, CTO of Invergent:
“We’re seeing a democratization of technology. A few years ago, running a competent model locally was only feasible for research labs with huge budgets. Today, thanks to advances in hardware, quantization, and software optimization, with DenseMax we can offer computing power that exceeds the needs of most business applications. We found that for 80% of specialized tasks, a well-calibrated local model is faster and far more cost- and performance-efficient than a massive generic cloud model.”
What We Learned: Myths and Realities of Local Performance
Our experiments debunked the myth that local AI is only for hobbyists. Still, success depends on understanding the technical nuances.
Local AI performance is possible thanks to quantization—a process that slightly reduces the precision of mathematical values in the neural network. While this marginally decreases theoretical accuracy, the performance gains and reduced VRAM requirements are exponential. AI infrastructure company Modal estimates about 2GB of VRAM is needed per billion parameters at half precision (16-bit). This is where hardware optimization comes in: “The sweet spot? Often, previous-generation enterprise hardware beats new consumer GPUs on VRAM per dollar,” emphasizes Ramon Perez from Jan.
While training large models in the cloud has a massive carbon footprint, the impact of inference (day-to-day usage) is often overlooked. U.S. data centers are projected to consume over 9% of the nation’s electricity by 2030, according to EPRI, and a single chatbot conversation can use roughly half a liter of water for cooling. Running inferences locally on efficient hardware dramatically reduces the carbon footprint per task as workload volume increases.
Hardware advances alone would not suffice without software evolution. Georgi Gerganov, creator of the ggml (llama.cpp) library, revolutionized accessibility. Platforms like Ollama or vLLM now allow preconfigured models to be installed with a single command line, eliminating the need for advanced programming knowledge.
The fundamental question remains: can smaller local models compete with cloud giants like GPT-4? Andriy Mulyar, founder of Nomic, argued that local models are excellent for personal tasks, but that the knowledge embedded in a 20B-parameter model is insufficient for enterprise-wide needs.
Our observation partially contradicts this. It’s true that larger models will always be smarter in general. But most companies don’t need a model that can write a Shakespearean sonnet and explain quantum physics in the same query. They need excellence in narrow domains.
This is where RAG and fine-tuning come in. By leveraging their own databases, a specialized local model can outperform a generalist in specific tasks. The quality of open-weight models is rising exponentially. “The quality differences are shrinking very fast,” says Gerganov. “Today, local quality is equal to—or better than—the cloud quality from just a few months ago.”
For forward-looking companies, the strategy is no longer cloud versus local, but cloud and local. For generic, low-risk tasks, the cloud remains a viable option. But for internal operations, for protecting intellectual property, and for controlling costs, investing in a local solution like DenseMax becomes a key strategic decision. The era when total control of AI was reserved for a handful of tech giants is coming to an end.
Enterprise AI that delivers: secure, sovereign-by-design, predictable, at scale. From POC to production with DenseMAX appliances, Digital Automation, and Surrogate Agents.
