Today an editorial focusing on the current "LLMs On Premise" inferiority respect the cloud services.

  1. Top-Tier Hardware Can Still Feel "Slow" and "Underutilized"

It is widely assumed that purchasing the most powerful consumer GPU available will solve performance latency in AI tasks. However, it is surprising to find that raw computing power (FLOPS) does not automatically translate into a responsive developer experience.

• Why this is noteworthy: It challenges the hardware-centric view of AI development. Even with a 5090 GPU—representing the cutting edge of local hardware—the actual utility can feel inferior to cloud services because the software stack cannot feed the GPU fast enough or intelligently enough.

• Supporting Quotes:

◦ "A hardware coder has expressed frustration with the performance of large language models (LLMs) running locally on a 5090 GPU. Despite the powerful hardware, the models seem underutilized..."

◦ "On paper, a 5090 GPU should make local LLMs feel instant and powerful. In practice, if the software stack is immature... developers will default to cloud-based IDEs..."

  1. The Value of AI Has Shifted from the Model to the "Orchestration"

The sources highlight an unexpected reality: the intelligence of an AI assistant currently relies less on the model's raw brainpower and more on its ability to access external files and tools. Local LLMs often fail not because they are "dumb," but because they are isolated.

• Why this is noteworthy: It suggests that "context" is more valuable than "compute." A local model feels "cramped" because it lacks the "quality of life" integrations (plugins, retrieval mechanisms) that cloud environments have mastered.

• Supporting Quotes:

◦ "The local models are described as unable to seamlessly use external tools to expand their effective context."

◦ "The coder’s frustration underscores how much value sits above the model layer: orchestration, retrieval, plugins, and editing environments can matter more than pure FLOPS."

  1. Local AI Demands "Engineering" Rather Than Just "Installation"

While cloud AI is a product you consume, local AI is described as a system you must engineer. The insight here is that achieving "control" over your AI comes with a heavy operational tax that many developers are unprepared for.

• Why this is noteworthy: It contradicts the narrative that local AI is becoming "plug-and-play." Organizations or individuals wanting to move off-cloud for privacy or control must be prepared to build their own retrieval and context-management infrastructure to make the expensive hardware actually useful.

• **Supporting Quotes: **

◦ "Local LLMs promise control but demand more engineering to become truly useful."

◦ "Unless local deployments are paired with mechanisms to bring in external context and tools, they will feel cramped compared to cloud offerings.

In the next days, we will deepen this "Compared to cloud inferiority" syndrome investigating if it is really true in these terms (quick answer: it is) and how to reduce the gap without needing a loan.

Davide