Technology is moving quickly. Models are releasing almost daily, after previously arriving weekly and then monthly. The speed is increasing, and everybody can feel it. A model like Qwen 3.8 27B was released a little over six weeks ago and remains an impressive option to run on local hardware. Comparing this model—which many now take for granted—against the most powerful models from a year ago shows how the numbers stack up. If the 27B model had come out a year ago, it would have been the best model in the entire world by a large margin.
This perspective highlights how much current AI capabilities, including what runs on accessible hardware, are taken for granted. Local AI remains worth pursuing even as some prices continue to drop and models grow more powerful. While some people discuss using frontier models, many others cannot or should not rely on them.
The Rapid Pace of Model Releases
Before, changes felt quick when they occurred yearly or every six months, with release cycles that were yearly. Even keeping up with Ubuntu releases twice a year once felt difficult. Now, content is released every single day. Going offline for three days could mean missing a handful of frontier models. Things arrive faster and faster, making it useful to zoom out and compare against just a year ago.
Local Models Compared to Last Year’s Frontier Options
A chart comparing frontier models from a year ago—such as GPT-5, Claude 4.3, and Grok 4—against Qwen 3.8 27B shows a striking difference. Some dismiss local hardware as not worth it, yet the comparison is against results from only one year ago. What is available now would have blown away anything the frontier models could have produced even a year ago. This illustrates how quickly local capabilities are developing.
Qwen 3.8 27B compared to all the state-of-the-art models from one year ago makes the point clear. If 27B had been released a year ago, it would have been the best model in the world by a large margin. One comment on the topic noted that a 27 billion model that runs locally is 35% more intelligent than the leading state-of-the-art model a year ago, and asked what will run locally next year.
GPT OSS 120 billion, with only 5 billion active parameters, was the eighth-best model in the world a year ago. It was described as a truly extraordinary model. By most standards now, a 27B dense model runs circles around it. In one year, a 120 billion model is being outperformed by a 27 billion dense model. This shows how quickly the landscape has shifted.
Local Hardware Remains Relevant
Some predict that hardware will go obsolete. Machines currently in use include one with a 1590 and another with a 3090. The 3090 Ti was a cutting-edge card six years ago and is still 100% relevant today. Claims that hardware will be dead in three years are false. Current setups run capabilities that would have been absolutely cutting edge a year ago. Even while discussing 27B, more powerful options are already running on the same hardware.
Models continue to get smaller yet more powerful. More is being squeezed out of local hardware every single day, which benefits end users. Looking ahead to what will run in a year is striking given this trend of smaller, more capable models.
Why Data Should Stay Local
One major reason to run AI locally involves data handling. Cloud platform documents may state that data is retained for only 30 days and that it is not used for training. This is a bold-faced lie, as is widely recognized. Data-related issues have appeared repeatedly. One recent example involved a fabled data set purchased from a Chinese LLM router containing six terabytes of data that included keys allowing access to over seven Chinese government entities and nine top Chinese firms. This came from a single data set.
Thinking that providers such as Anthropic are not training on user data is unrealistic. Time and again, issues have surfaced, including cases of OpenAI stealing from mathematicians. This is not new. As capabilities advance rapidly and power is squeezed into smaller boxes faster and faster, the decision to send data elsewhere carries ongoing risks.
Some point to options like Haiku 5.5 running nearly free as a reason not to build local setups. While the model is powerful, this approach still pumps data into the cloud. For someone with nearly 30 years in software development, that is not acceptable. People in regulated industries are often not allowed to do it either.
Four years ago, before the GPT-3 moment, proposing to stream an entire system, all databases, and every piece of customer data through a third-party service would have been seen as nuts. Now that AI is powerful, some treat it as acceptable. Best engineering practices that have existed for over three decades, four decades, and longer argue against giving this data away. Data should stay local.
Local-First Frameworks and Ownership
OpenMonoAgent.ai was launched as a framework supporting this approach. Base principles include local first, always unlimited tokens, and a zero-cost sandbox by default. It is open source so others can examine it. It is not meant to be a subscription. The infrastructure should be owned and sitting on a desk rather than rented.
Three major new features are launching on OpenMonoAgent. Regardless of the specific framework chosen—whether that one, another option, or a custom harness—running AI locally is worthwhile. OpenMonoAgent offers a practical place to start for those unsure where to begin. Many people in regulated industries cannot simply pump data into the cloud.
Treating AI as Owned Infrastructure
Most companies do not have a technology problem. They have a leadership problem, and they pay for it in missed deadlines, failed integrations, and AI investments that delivered nothing. The businesses winning right now are not the ones chasing the latest AI trends. They are the ones that built on solid engineering and treated AI as infrastructure they own, control, and integrate into software that actually works.
This means real custom software development: database architecture, API design, system integration, and scalable infrastructure built the right way, as it has been done for over 25 years. When AI belongs in a solution, it is built in rather than bolted on or wrapped around someone else’s API. It is designed into the architecture, running in the user’s environment rather than a vendor’s cloud or on someone else’s pricing schedule.
An open-source platform called OpenMonoAgent.ai demonstrates this model. It is a terminal-native AI coding agent running entirely on local LLMs, with zero API costs, zero telemetry, and full ownership. It is growing because serious engineers recognize real infrastructure when they see it. The same standard of leadership can be applied without full-time executive cost through strategic architecture, hands-on delivery, and clear accountability.
Organizations ready for custom software built to last, with AI integrated where it actually makes sense, can focus on technology leadership and AI as true infrastructure that produces real, ownable results.
Conclusion
Local AI has advanced to the point where a 27B model that runs on ordinary hardware would have outperformed last year’s leading frontier systems by a wide margin. At the same time, models continue to grow smaller and more capable, and hardware from six years ago remains fully relevant. Sending data to the cloud still means giving it away—something best engineering practice has advised against for decades. Running AI locally keeps that data under your control and treats the models as infrastructure you own.
