Large Language Models: Running Locally vs Using Online — Where's the Real Gap? ​

Written in September 2026. AI technology moves fast, so the model capabilities, prices, and hardware requirements mentioned here reflect the situation at that time and are for reference only.

Most people first meet AI through chat tools like ChatGPT, Qwen (通义千问), or DeepSeek. Naturally they wonder: could I move a large model onto my own machine too — no fees, no privacy worries?

The answer is: it runs, but the experience has a clear gap with top online services.

This article neither hypes nor trashes it — an honest look at what local LLMs are really capable of, what they can and can't do, and who they suit.

The conclusion first ​

A local LLM is enough for light everyday use, but it can't replace top online services.

Specifically:

• Writing, translation, polishing, summarizing: a local model is basically sufficient
• Writing simple code, fixing bugs: usable, but not as smart as online services
• Complex reasoning, deep analysis, multi-step planning: a clear gap — online services are stronger
• Agents: most people choose a "local framework + online API" combination

The reason is practical: consumer hardware simply can't support the full capability of a top-tier large model. VRAM, bandwidth, and system RAM are all bottlenecks.

But local LLMs have irreplaceable value too — privacy, no cost, offline availability, customizability. The key is to work out: is what you need within the capability range of a local model?

Which large models can you run locally? ​

Most mainstream open source large models can run locally (depending, of course, on whether your hardware is up to it).

Extra note: Chinese open source models generally handle Chinese better than foreign ones, so users in China should look at the Qwen, DeepSeek, and GLM series first.

These models come in different "sizes" — from 1B (1 billion parameters) to 70B+ (over 70 billion parameters). More parameters means more capability, but also higher hardware demands.

How do you run an LLM locally? ​

To run a large model locally, these are the main tools:

For beginners, Ollama is the most recommended starting point — easy to install, comprehensive model coverage, active community.

Where exactly is the gap between local and online? ​

1. Model capability: a clear gap ​

This is the most fundamental gap.

Top online services (such as GPT-4o, Claude Opus, and in China the Qwen flagship and DeepSeek online versions) use the largest and strongest models, with parameter scales and training data volumes far beyond what consumer hardware can run.

Where exactly they differ:

• Complex reasoning: math problems, logic puzzles, multi-step analysis — online services are much stronger
• Coding ability: complex projects, multi-file work, tricky bugs — online services are more dependable
• Long-text understanding: handling documents of several hundred thousand words — the online experience is better
• Multimodal ability: mixed image-and-text understanding, video understanding — online services are stronger
• Tool calling: invoking search, code execution, and other tools to complete complex tasks — online services are more mature

What local models do well:

• Everyday writing, polishing, translation
• Summarizing information, organizing documents
• Simple Q&A, knowledge lookups
• Writing simple code snippets
• Role-play, creative brainstorming

Put simply: for 80% of everyday scenarios, a local model is enough. But for that 20% of complex tasks, the gap is obvious.

2. Hardware limits: the root cause ​

Why can't local models keep up on capability? Because the hardware can't.

Top-tier large models have hundreds of billions of parameters and need hundreds of GB of VRAM to load in full — a consumer GPU simply can't hold them. So what runs locally is always a "small model" (7B to 32B parameters), or a "quantized" version of a larger one.

What is quantization? In short, it "compresses" the model so it fits into less VRAM, at the cost of reduced accuracy. Just as an over-compressed image turns blurry, an over-quantized model gives lower-quality answers.

Beyond VRAM there are two more bottlenecks:

• Memory bandwidth: consumer GPU memory bandwidth is far below that of professional data-center GPUs, so inference is much slower
• System RAM: loading a large model needs a lot of memory; below 32GB, anything on the larger side becomes a struggle

3. Speed: online is faster ​

Online services run on enterprise hardware and respond quickly. Running a large model locally — especially a bigger one — is noticeably slower in generation; sometimes you wait a few dozen seconds or more for an answer.

Local does have an advantage, though: no queue and no rate limits. Online services may throttle you at peak times; locally you use it however you like.

4. Privacy and security: local wins outright ​

This is the biggest advantage of a local LLM.

Every conversation stays on your own computer, with no data uploaded to any server. Well suited to handling:

• Internal company documents and trade secrets
• Personal private content
• Research material that can't be shared externally
• Data from sensitive industries
• Teaching data and student information at schools and training institutions

For enterprises, schools, and professional users, privacy and data security alone is reason enough to deploy locally.

5. Cost: free vs paid ​

Local models: free. Download and use — no monthly fee, no call limits.

Online services: free tiers have limits, paid tiers range from a few dozen to a few hundred yuan a month, and API usage is metered on top of that.

But note: running a large model locally needs a decent GPU, which is also a cost. The difference is that a GPU is a one-time investment — after that, however long you use it, it costs nothing.

Hardware requirements ​

Large language models sit between TTS and video generation in hardware demands.

The situation on Macs with M-series chips:

Macs handle local LLMs reasonably well, because Apple Silicon's unified memory architecture is fairly friendly to large models. For M1/M2/M3 Pro, Max, and Ultra versions, the more memory the better.

But the problems with Macs are:

• Slower than an NVIDIA card at the same price point
• Some tools and plugins are less compatible than on Windows + NVIDIA
• Hard to upgrade (memory is soldered on, with no way to expand later)

Overall, a Mac running local LLMs is usable, but it isn't as expandable as Windows + NVIDIA.

Agents: most people choose a hybrid ​

Agents have been the hot concept of the past couple of years — letting AI plan tasks autonomously, call tools, and accomplish complex goals.

Many people assume an agent has to run entirely locally, but in practice: most people use a hybrid "local framework + online API" setup.

Why? Because agents place high demands on a model's reasoning, planning, and tool-calling abilities. A small local model driving an agent often runs out of "brain" — unclear planning, tool-calling errors, and drifting off track over multiple turns.

So the common combination is:

• The agent framework runs locally — for example Dify, n8n, or AstrBot, which you can deploy yourself
• The model comes from an online API — for example the Qwen API or DeepSeek API, calling on flagship-level capability
• Data and logic stay local — your knowledge base, workflows, and business logic all remain in your own hands

This keeps the control and privacy of local deployment while still getting top-model capability.

Of course, if you have a high-spec machine (24GB+ VRAM), you can also try a fully local agent — with a 32B or larger model, results improve considerably. For most people, though, the cost is a bit high.

Who is it for, and who is it not for? ​

People suited to a local LLM ​

• People who value privacy: sensitive conversation content you don't want uploaded to any server
• Heavy users: you use AI a lot every day and online service fees are too high
• Technology enthusiasts: you're interested in AI technology and want to study models, do fine-tuning, and build apps
• Users in offline environments: poor connectivity, or no internet access allowed — local is the only option
• People building private applications: knowledge bases, intelligent customer service, internal tools that need private deployment
• Students and researchers: learning how large models work, running experiments and graduation projects
• Universities and training institutions: building AI teaching labs so students can get hands-on with large models
• Teachers and teaching staff: lesson preparation, setting questions, marking assignments — with data never leaving campus

People not suited to a local LLM ​

• People chasing the strongest capability: if you need the best reasoning, coding, and multimodal performance, an online flagship model is the first choice
• People who don't want to touch technology at all: you just want to open a web page and chat, without installing anything
• People with low-spec computers: under 4GB of VRAM, you can't run anything respectable
• Occasional users: a few times a month — you won't even use up an online free tier
• People building complex agents: local models aren't capable enough; use an online API instead

Why should schools and training institutions care about local LLMs? ​

In education settings, local large models have distinctive value.

Teaching needs ​

Students in AI-related majors can't learn how large models work from textbooks alone — they have to run and tune one themselves to really understand it. A locally deployed large model lets students:

• Directly feel the capability differences between models of different parameter scales
• Get hands-on with model fine-tuning and prompt engineering experiments
• Build small applications on top of local models for course projects and graduation projects
• Learn cutting-edge techniques such as RAG knowledge bases and agents

If everything goes through an online API, students can only "use" the model, and it's hard for them to understand what a model "is" and "why".

Data security and compliance ​

Schools and training institutions hold large amounts of teaching data, student information, and exam question banks. Uploading these to a third-party online service carries privacy and compliance risks.

With a locally deployed large model, all data stays inside the internal network and is never transmitted externally — a better fit for the data security requirements of the education sector.

Cost control ​

Buying online service accounts per student adds up over a semester. And usage varies enormously between courses and year groups, making it hard to estimate.

Set up a local environment once, share it across the whole school afterwards, and the long-term cost is lower and more predictable.

A unified environment ​

When every student installs their own environment, problems are inevitable — wrong versions, dependency conflicts, different operating systems... Teachers end up spending half their time fixing environment issues.

When the school sets up one local LLM environment, every student uses the same configuration, the same models, and the same tools, and teaching is far more efficient.

How to choose? A practical suggestion ​

Don't agonize over all-local versus all-online — for most people the best answer is a combination:

• Light everyday tasks (writing, polishing, translation, simple Q&A) → local models, free and private
• Complex tasks (deep analysis, complex code, multi-step reasoning) → online services, with assured capability
• Agents and automation → local framework + a Chinese online API (Qwen, DeepSeek, etc.), balancing capability and control
• Teaching and experiments → primarily local, with online services as a supplement, so students can both get hands-on and experience top-tier models

Combined this way, you save most of your everyday usage costs without falling short at critical moments because the model isn't capable enough.

There are many online LLM services to choose from in China — Qwen is well-rounded, DeepSeek is strong on reasoning and code, Kimi excels at long text, GLM has a complete ecosystem, MiniMax is good at multimodal — each with its own strengths, so pick according to your needs.

Where do you start? ​

If you want to try a local large language model, here's how to get started:

1. Check your computer's specs — GPU VRAM and system RAM
2. Start with Ollama — the simplest installation, the most complete model coverage, beginner-friendly
3. Run a small model first to test the water — for example Qwen 7B or DeepSeek 7B, and see whether the experience is acceptable
4. Upgrade based on the experience — if it's not enough, try a larger model; if it is enough, keep using it
5. Explore advanced options — RAG knowledge bases, agents, local app integration...

With 魔当 you can install apps such as Ollama, LM Studio, and Chatbox AI in one click, without wrestling with environments and dependencies yourself.

A local large model isn't a cure-all, but it gives you another option — free, private, and under your control. If those matter to you, it's worth spending some time trying it.