What Is Hybrid AI ? How Combining Cloud and Edge Architecture Optimizes.
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Quick Summary
- Hybrid AI combines Edge AI and Cloud AI within a single PC system to manage different types of workloads seamlessly.
- It leverages the advantages of both architectures to prevent data leaks, eliminate unstable outputs, and optimize your cloud service budget.
- It offers flexible LLM choices, allowing users to always deploy the right model for their specific tasks.
In our previous article, we introduced What Agentic AI can do on AI PC and explored its use cases in our daily lives. We also discussed how Agentic AI can operate in both edge AI and cloud AI enviornments. Each approach offers great performance and results, but each also comes with its own constraints. This is where hybrid AI can provide a new solution, helping to strike a balance between edge AI and cloud AI. Let’s take a closer look at why we need hybrid AI and how it can deliver the best of both worlds.
Why Do we need Hybird AI?
Cloud AI offers much more powerful computing capabilities, which means it can deliver remarkable results and handle more demanding workloads. However, it often comes with additional costs. Such as subscription fees or API costs for the AI services.
Moreover, not every type of question or task can be handle by cloud AI. Certain tasks or requests may be restricted due to regulations, privacy concerns, or other specific requirements. One of the biggest concerns is data privacy: users’ data may be processed or used by AI service providers to improve their models.
Another issue is that online AI models are constantly being updated or changed. As a result, the answers they provide may also change over time, making it difficult to ensure that the same question will always produce the same result or that an answer will remain accurate.
Edge AI, in contrast, offers almost the opposite approach to cloud AI. It may not always produce the same level of results as a powerful cloud-based model, but it provides several important advantages. Users don’t need to pay additional subscription feees for cloud AI services, and their data can remain on the device, reducing the risk of data exposure. In addition, locally running AI models can provide a more predictable and controlable environment, while giving users greater flexibility over the tasks and questions they want to handle.
By combining the advantages of both approaches and intelligently assigning suitable tasks to the appropriate AI service, we can maximize the performance of an Agentic AI PC.
For example, more demanding tasks can be assigned to cloud AI to take advantage of the its greater computing power, while common and privacy sensitive tasks, such as translation, summarization, scheduled jobs, and processing personal information, can be handled by edge AI.
We can also use edge AI to brainstorm ideas and develop initial concepts, then leverage cloud AI to turn those ideas into more sophisticated results. By handling the initital stages locally, we can reduce uncessary cloud AI usage and save tokens or credits, while still taking advantage of the cloud’s greater computing power when it matters most.
By making sure that each computing resource is used wisely, we can achieve a better balanced between performance, cost, privacy, and flexibility.
Spend Every Coin in the Right Place
By taking advantage of cloud AI’s capabilities, we can assign high value and compute-intensive tasks to cloud AI while letting edge AI handle more general workloads.
Cloud AI can generate high quality programs or dashboards for data analysis. Once the program has been developed, we can use an AI agent to import sensitive or confidential data and perform the analyze locally. In this way, sensitive data can remain on the device, reducing the risk of data exposure.
This approach is similar to the concept of outsourcing software development. Cloud AI can help us build the tools we need, while edge AI can handle the sensitive data and actual analysis locally. By combining the strengths of both, we can make better use of our AI resources while maintaining greater control over data privacy and security.
Use the Model (LLM) You Need for the Task
Cloud AI can help answers many of the questions you ask and handle a wide range of tasks. However, not every type of question or request can be answered. Due to certain regulations, policies, or other restrictions. You may not always receive a complete answer, and some requests may be declined by cloud AI services. In contrast, edge AI can offer greater flexibility because locally running models are not subject to the same service-level restrictions as cloud-base AI services.
In these situations, we can look for a suitable LLM on Hugging Face that fits our specific needs and run it locally on an AI PC. Hugging Face is an open platform that provides access to a wide range of open source and openly available AI models that can be used for different applications. Such as ollama, or Bionic.
For more MSI agentic AI PC information, please visit here for more detailed information.
https://www.msi.com/Business-Productivity-PCs
https://www.msi.com/Business-Productivity-PCs
For research and development, we can use local LLMs to study and analyze the programs we need, and then use cloud AI services to help develop or refine a high quality final product. As we mention earlier, some request may be restricted by the cloud AI services. In such cases, we can use an edge AI LLM to analyze the code, identify potential issue, optimized the code, or further enhance the product.
By combining the strengths of local and cloud AI, we can make the development process more flexible and potentially achieve better results.
How Can We Choose an Agentic AI PC?
MSI PRO MAX Edge AI+ is engineered specifically for Agentic AI applications. Powered by the built-in AMD Ryzen AI Max+ 395 processor, it is fully equipped to execute Large Language Models (LLMs) and meet demanding AI software requirements. With up to 128 GB of unified memory, it smoothly runs 120B parameter models locally at over 30 tokens per second delivering ultra fast AI response times.
Large Language Model (LLM)
VRAM Usage
Avg TPS (tok/sec)
Gemma4 E4B
9.6GB
50.51
Gemmar4 12B
7.6GB
23.24
GPT-OSS 20B
13GB
44.36
Gemma4 26B
17GB
51.85
Qwen 3.6 35B
23GB
58.96
GPT-OSS 120B
65GB
33.29
PRO MAX Edge AI+ LLM performance table.
Beyond AI workloads, the built-in Radeon 8060S graphics engine handles 2K gaming effortlessly. Concerned about thermal throttling on a compact system? MSI has engineered a custom triple-fan cooling system with a pure copper heat spreader to ensure your system runs cool and stable. Its compact footprint fits easily into any workspace. Click here to learn more about the MSI PRO MAX Edge AI+.
PRO MAX Edge AI+: https://www.msi.com/Business-Productivity-PC/PRO-MAX-EDGE-AI-Plus-11M
