Local Inferencing (LLM)

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Cloud services will eventually fail as they are overpriced and costly to run. Local inferencing allows you to generate more tokens given a fixed hardware cost and an extremely low electricity cost compared to token consumption from cloud services. Large Language Models are evolving fast, and they don’t have to be in a costly data centre to run. Right now you can comfortably run an open-weight LLM on your 16GB memory device and achieve reasonably good level of intelligence compared to the closed-source frontier models. This is the right choice for any user who treats an AI agent as a daily work driver.

Local inference keeps your data secure. If you run a business with confidential requirements where your data must stay on premise, local inferencing is your only choice. It brings AI capability into the team without sending material outside the perimeter.

Local LLM selection:

Choosing the right local language model for the team's context.

What's certain about LLMs is that they are becoming increasingly smarter, lighter in size so they fit into smaller machines, and more specialised that each model have their own expert domain. New models get published everyday, and choosing the best one is always important. Mana Agency will help you filter out the irrelevant ones so you can design your local inferencing plan around the models that fit your context.

Hardware selection:

Choosing the right hardware for the deployment.

Hardware is the most important investment for the agentic systems. There are many hardware options that would be able to run your preferred language model, but what is important is the hardware that can get you covered for the next couple years and fits your budget. We will help you figure out what is available for you right now, and assess how it will perform when new AI models and technologies get released in future.

Local Inferencing is the foundation of a full agentic deployment. A complete build delivers five things: local LLM selection, hardware selection, agent system implementation, agent profile setup, and user-side setup with team training. This page covers the first two. The other three — agent system implementation, profile setup, user-side training — ship as the Agentic System Build on top of this foundation.

  What you walk away with from Local Inferencing:

• A chosen local language model, configured for the team's context.
• A chosen hardware specification, sized for the workload.
• An integration path — how this foundation connects to the rest of your build.
• A clear read on what your agent will and won't be able to do, before any further commitment.