September 2025 Latest Local AI Honest Review - Clear Limitations but Essential Practical Uses
September 2025 Latest Update
Interest in local AI is rapidly growing due to stricter usage limits on AI services like ChatGPT and Claude, along with increasing security concerns.
While cloud-based AI services like ChatGPT, Claude, and Gemini offer excellent performance, since 2025 began, daily usage limits have become even stricter, and more people are urgently seeking alternatives due to data security issues.
This article is based on the most current information as of September 2025 and provides a comprehensive overview of local AI setup and real-world usage experiences.
Current Reality Check as of 2025: What We Need to Honestly Discuss
Let me start with the realistic perspective.
As of September 2025, many users commonly point out that “when you actually try to use it, you immediately hit limitations and end up going back to online services.”
Experts in the field believe that “local LLM performance cannot match ChatGPT, and there’s a huge gap between cloud and local LLM performance.”
However, there are clear reasons to consider local AI:
- Cost-effective usage
- Most importantly, no need to send your data to external servers, providing security advantages
This can be an attractive alternative, especially for enterprise environments handling sensitive data or personal creative work where you want AI without censorship.
Major Local AI Platform Comparison as of September 2025
As of 2025, there are three most popular local AI platforms:
Ollama (2025 Latest Version)
Currently the most popular. Many people are using Ollama GUI these days, and the interface has become even more intuitive with recent improvements.
The Turbo feature allows web data collection, which helps compensate for some limitations of local AI.
LM Studio
Particularly well-regarded for coding-related tasks.
Users report especially good performance on MacBooks, with excellent detailed configuration support.
Jan
Provides an easy-to-use UI, but updates are somewhat slow, which is disappointing.
Hardware Requirements: Realistic Investment Costs
Hardware specifications are the most important factor when building local AI.
Based on actual usage experiences, here’s what you need:
Minimum Specifications
- Windows: GTX 3060 or higher
- Mac: M1 or higher
But these are truly just the ‘minimum’ specifications.
Practical Specifications
To really use it properly, you need significant investment.
Even for EEVE 10.8B, you need at least RTX 3080 for decent performance. When you consider the cost and power consumption, you might wonder if it’s really “free” just because you don’t pay usage fees.
For this kind of investment, subscribing to ChatGPT or Claude might be much better.
Recommended Specifications
- VRAM 20GB or more to run 13B models smoothly
- RTX 3090 used (24GB) popular for cost-effectiveness
- Memory: minimum 16GB, recommended 32GB or more
- Radeon graphics cards also work fine for LLM operation
In practice, with RTX 4090 24GB, ChatGPT OSS 20B models are about the practical usage level.
Korean Language Support Models: Latest Status as of September 2025
As of 2025, Korean-focused models have significantly improved.
2025 Recommended Models
- EEVE-Korean-Instruct-10.8B-v1.0: Developed by Yanolja, enables natural Korean conversations
- KoAlpaca: Recently popular Korean-optimized model
- Upstage SOLAR Series: Developed by Upstage
- KoGPT: Various derivative models available
However, in actual use, English prompts seem to produce better results than Korean questions.
For Developers: Usage Options
VSCode integration is an important point for developers.
Ollama can be connected to VSCode like Cursor does.
However, for actual coding applications, using something affordable like DeepSeek API is better in terms of time and energy.
Developers around me who have adopted local AI rate local AI satisfaction as very low - the performance gap compared to ChatGPT or Gemini is so large it’s incomparable.
Personal Data Training: Possibilities and Limitations
One of the big attractions of local AI is the ability to train it on your personal data.
If you train it on numerous work-related materials stored on NAS and use it for various scenarios, it can be so good that you don’t feel the disadvantages of local AI.
However, experts believe that serious training on personal PCs or Macs is still quite difficult.
You need cloud or dedicated machines. For simple methods, conversational learning through file upload and chat is the most realistic option.
For more professional training, you can use PrivateGPT and Python, but this requires separate technical expertise and isn’t easy.
Special Use Cases
Local AI can be freely used for content that online services restrict.
With local AI, you can disable local censorship, allowing you to freely continue creative conversations that cannot be done with ChatGPT or Claude.
One creator mentioned that content created with local AI received better reader responses.
Also, it can be used during overseas business trips in environments with limited internet access.
Being truly local, Ollama supports airplane mode for offline use. (But do we really need to use it this way?)
Performance Optimization Tips
Performance optimization methods found through actual use:
- Choosing the right model size for your PC specifications is most important
- For first-time users, starting with Qwen 4B model is recommended
- Mac computers recommended over Windows PCs
- Combine with online data collection to compensate for shortcomings
- Pre-training with professional materials ensures uncontaminated results
Cost-Effectiveness Analysis
Local AI setup requires significant initial investment.
For proper performance, graphics cards alone cost over $2,000.
When you include electricity costs, it’s certainly not “free.”
As people point out, to properly utilize local data training without exposing your data, equipment setup alone can cost tens of thousands of dollars.
So if your only goal is simple cost reduction, using online AI subscription services might be much more economical than considering local AI.
Conclusion: When Should You Choose Local AI in 2025?
As of September 2025, local AI doesn’t solve everything.
Performance-wise, it significantly lags behind cloud AI, and initial setup costs are substantial.
However, local AI is worth considering in these cases:
- Security-first work environments: When you cannot send sensitive data externally
- Uncensored creative activities: When you don’t want to be constrained by online service content policies
- Specialized field applications: When you need customized AI trained on accumulated professional materials
- Long-term cost savings: When you need continuous, large-scale AI work
- Technical learning purposes: When you want to understand and experiment with how AI models work
Looking toward 2025 and 2026, as hardware performance improves and more efficient small models emerge, local AI usage will likely increase further.
As of September 2025, while it’s not a perfect alternative, it’s certainly a valuable option for specific purposes.