The Advantages of Running Models Locally

Local AI is no longer limited to “huge models or nothing”. Smaller and specialized models have improved significantly. For many application scenarios like summarization, rewriting, extraction, classification, or domain-specific assistance a smaller model can be surprisingly capable, especially when combined with good prompt design and tool integration.

Running models locally is becoming a practical and strategic choice, because the benefits are clear:

• Cost: Cloud AI services are powerful, but usage-based pricing can be difficult to predict in real applications, especially once AI features are used frequently by end users. Running models locally can eliminate these costs entirely and makes AI usage easier to scale without surprises.
• Privacy and control: Many applications handle sensitive user data, such as documents, internal notes, customer information, or proprietary knowledge. Even with trusted cloud providers, some customers and industries require that prompts and context never leave the local machine or internal network.
• Offline: This matters not only for fully disconnected environments, but also for reliability. A local model keeps working even if a service is down, rate-limited, or temporarily unreachable.

Ollama or llama.cpp

Ollama uses llama.cpp internally, and TTMSMCPCloudAI already supports Ollama as an AI service. So why add llama.cpp as a separate service and which one is better?

Our goal is simple: provide flexibility. There is no universal “better” option. Ollama focuses on convenience and ease of setup while llama.cpp offers deeper configurability and often better performance.

Both Ollama and llama.cpp can run models purely on the CPU, without requiring a dedicated GPU. For many smaller workloads, this is already sufficient and avoids additional hardware investment. With a consumer GPU, models can run at least 5× faster, making local hosting suitable for chat, coding assistance, and larger workloads.

We ran some models ourselves on our Windows office machines to better understand the differences and see how far someone can get with avarage hardware. We used the prebuilt llama.cpp binaries and observed the following patterns:

The differences largely depend on the hardware, workload, and configuration. Systems running Linux with an NVIDIA GPU might deliver better results due to driver maturity and broader optimization support. However, the safest approach is to test both in your own environment and choose the one that fits your setup best!

Conclusion

By supporting multiple local runtimes, TMS AI Studio gives you greater control over deployment decisions, helping you address key requirements such as cost, data privacy, offline availability, and infrastructure independence. You can continue using the same workflow and component interface while gaining even more flexibility in how and where your models run.

As local AI continues to evolve, our goal remains the same: providing developers with practical, flexible, and production-ready tools to integrate AI into real-world applications.