Contributing expert: Anton Cheplyukov,
AI Practice Lead at Coherent Solutions
 

When a Cybernews' seasoned journalist explored how tech professionals are adopting local AI models, Anton Cheplyukov, AI Practice Lead at Coherent Solutions, offered a practical perspective. Developers are increasingly running AI locally to prototype faster, cut costs, and protect data. But there are trade-offs.

 

Local AI isn't replacing the cloud, but it's reshaping how innovation happens. Teams can rely on accessible hardware and open frameworks to stay agile and test ideas quickly without depending solely on cloud APIs.

Pros of running AI locally

 

  • Privacy control

  • No subscription costs

  • Faster response times

  • Offline capabilities

  • Rapid iteration 

 

Cons of local AI models:

 

  • Lower accuracy

  • Hardware constraints

  • Maintenance overhead

  • Limited scalability


Anton noted that local models are "much lower from an accuracy perspective," so they are not yet ready for client deployments. 

AI models that tech pros are using


Cybernews outlined a variety of tools and frameworks gaining traction among professionals:

  • Ollama (Qwen3, Gemma3) is used by Coherent Solutions' team for cost-efficient and privacy-safe prototyping.

  • LM Studio is preferred for running language models offline on laptops for coding and content tasks.

  • LM Deploy serves lightweight models locally on enterprise servers.

  • Mistral 7B is a compact open-weight model for text generation and reasoning with minimal GPU load.

  • Phi-3 Mini is a preferred choice for fast, low-latency applications and tight memory budgets.

  • Qwen 2.5 Coder is favored by AI engineers for local code assistance without cloud dependencies.  


Anton's insights capture the balance between agility and accuracy, which is a principle that defines how Coherent Solutions drives digital value for clients.

Read the full story by Ernestas Naprys at Cybernews.