Which Local AI LLM Fits Your PC?

Sometimes it was quick and empty. Sometimes it was smart and so slow I walked away. Sometimes it ate so much memory the rest of my day started to limp.

So I stopped asking which model wins on the internet.

I started asking a simpler question.

Which local AI LLM fits this PC, for the person sitting here, and the work they actually do?

That question is why I built Haval LocalAI Bench.

Why it matters

Why a local benchmark matters

Cloud AI hides the machine. Someone else’s servers take the heat. On your PC, nothing is hidden. The model, the memory, the wait, and the rest of your day all share one desk.

A public leaderboard cannot see that desk. It cannot see that you still need Chrome open. It cannot see that a gamer wants an answer between matches, or that a parent needs a school email rewritten before dinner, or that an engineer needs correct code, not confident nonsense.

If you only trust the chart, you buy a race car for a grocery run. Or you buy a bicycle for a highway. Local benchmarking matters because the only fair track is the computer in front of you.

LocalAI Bench exists to turn that messy truth into a clear report: what fits, what fits well, what is too slow or too heavy, and for whom.

What this is

One chat. One model. Not a team of agents.

I want this part to be unmistakable.

This is not a multi-agent system. I am not sending a swarm of helpers to argue with each other. I am not building a company of bots.

The benchmark is closer to what you already do every day. You open a model. You chat with it. Tokens go out. Tokens come back. One conversation. One model. One PC.

A single-agent chat, this is like sitting across a table from one person and asking them to do a job. You do not invite five strangers to finish each other’s sentences. You ask one model the kind of question a real persona would ask, then you judge the answer, the wait, and what it cost the hardware.

That is the whole loop. Regular exchange. Real tasks. Measured on your machine.

Hardware first

The chart does not sit at my desk

A leaderboard is a race on a track I do not own. My desk has leftover room after the browser, the editor, the game, the meeting. VRAM, this is like the small counter in a kitchen where the cooking actually happens. System memory, this is like the whole kitchen. A model can fit on paper and still crowd the counter so badly you cannot make dinner.

So the report starts with the machine, not the trophy. What size range fits comfortably. What is a good everyday balance. What is the largest model that is still practical. Where the experience starts to feel slow or tight. Which setups I would not recommend at all.

I do not want a universal winner. I want a fit.

People

Three chairs at the same table

Fit for whom?

A student asking for an explanation is not the same as an engineer debugging code. A casual gamer who wants a quick tip is not the same as a creator writing scripts between takes. A parent rewriting a school email is not the same as a manager pulling next steps out of a messy status note.

So I split customers the way I see them.

Consumer. Home, school, family, shopping, writing, learning.

Gaming. Play, performance, content, and people learning to build small games.

Commercial. Real work across roles. I say commercial on purpose, not enterprise. Most people I care about are professionals getting a Tuesday done, not a logo on a slide.

There is no single best model across those chairs. One model can be right for Consumer and wrong for Commercial. That is the point.

The method

How I combine the pieces

Here is the simple version of how the benchmark thinks.

First I look at the persona. Who is this person. What do they actually do. What do they expect from the answer. How long are they willing to wait. A quick tip between matches cannot wait like a deep analysis of a long report. A support reply needs warmth and speed. A finance note needs numbers you can trust.

The persona sets the job and the patience.

Then I look at the model quality for that job. Not one vague “smart” score. Separate strengths, because different people need different muscles.

Thinking and reasoning, this is like whether the model can walk through a problem without getting lost. An executive and an engineer both need this, but they need different shapes of it.

Math, this is like a clean calculator that can also show its steps. Critical for finance and learning. Less central for a casual game tip.

Instruction following, this is like a cook who actually follows the recipe you wrote. Almost everyone needs this. A legal or policy persona needs it even more.

Structured output, this is like asking for a labeled folder, not a pile of papers. Developers, analysts, and operators need this a lot. A creative writer may need it less.

Coding, when the persona is technical, this is like whether the repair actually starts the car.

Those qualities are not weighted the same for every chair. A Rising Game Developer cares more about coding and clear explanations. A Family Coordinator cares more about practical plans and readable language. An Analyst needs math and structure to carry more of the score. A Casual Gamer needs fast, simple help more than a beautiful proof.

So the blend is always two layers.

1. What this person needs, expects, and will wait for.

2. How strong the model is at the skills that matter for that need.

Then I add the machine. Did it answer in a time that fits the persona. Did it finish reliably. Did the PC stay healthy enough to keep living beside it.

A fast wrong answer fails. A brilliant answer that breaks the persona’s patience fails. A model that loads and then starves the rest of the PC fails.

I combine those so one shiny strength cannot hide a serious weakness. That is how LocalAI Bench turns a chat into a judgment I would trust for someone else.

Workload

Light, balanced, heavy

Even one person is not one workload.

Light is the quick ask. It should feel almost immediate.

Balanced is the real day. More context. A few steps. I weigh this heaviest, because this is how people actually live with AI.

Heavy is the long haul. People will wait longer, but only if the result earns the wait.

Waiting twenty seconds for a tiny rewrite feels broken. Waiting a couple of minutes for a careful read of a long report can feel fair. Time has to be judged through the persona’s eyes, or the score lies.

The product

From install to confidence

LocalAI Bench is the application I wanted when I was tired of playing technician.

It should guide setup without asking anyone to love terminals. It should prepare the PC, confirm acceleration is really working, help find and download models, run these single-model chats as meaningful evaluations, and hand back a hardware-first report a person can read.

At the end I want the customer to know what can run, what can run well, which size range is the sensible everyday choice, which tested model fits Consumer, which fits Gaming, which fits Commercial, which personas and workloads feel good, where the experience starts to fall apart, and why.

That is confidence. Not a screenshot of a chart.