What hardware do I need to run a local LLM?

A 7B-class model runs on 8G VRAM or 16G RAM (Ollama / LM Studio); 70B-class needs multi-GPU or extreme quantization. Mac unified memory (M-series) offers great value.

What's the difference between Ollama and LM Studio?

Ollama is CLI-first, lightweight, suited to servers and scripts; LM Studio is a GUI with a model marketplace, suited to desktop trial.

Is a local model or an API more cost-effective?

For high-frequency heavy use, local amortizes a one-time hardware cost and wins; for occasional use, an API is cheaper. Factor in power, maintenance, and model updates.

Which open-source models can I run locally in China?

Qwen, DeepSeek, GLM, and Llama series can all be pulled in one line via Ollama / LM Studio, mostly VPN-free.

Local LLM Deployment Compared: Ollama / LM Studio / vLLM / llama.cpp / GPT4All-Which to Pick

Use APIs long enough and the anxiety creeps in: bills you can't control, data leaving the border, models getting deprecated on a whim. Running an LLM on your own machine is the "peace of mind" pick for more and more developers in 2026. But local deployment is deeper than it looks-Ollama, LM Studio, vLLM, llama.cpp, and GPT4All lined up, picking the wrong tool costs more than picking the wrong model. This piece field-tests five and hands you a table you can choose from.

Why Run Your Own LLM

Three reasons, getting more hard-core as you go:

Data stays in-house: contracts, medical records, internal code fed to a cloud API carry compliance risk; locally, the data physically never leaves.
Controllable cost: heavy API use runs up thousand-yuan monthly bills; local is a one-time hardware spend that gets cheaper the more you run.
No lock-in: when a cloud model raises prices, rate-limits, or shuts down, you're helpless; locally, you hold the weights and can always run them.

The cost: you need to know a bit about hardware (VRAM / RAM), wrestle with setup, and accept that local models are a tier below cloud flagships in raw ability.

Subjects and Method

Subjects (July 2026 versions):

Tool	One-line positioning
Ollama	Command-line native, the most popular local inference engine
LM Studio	GUI + model marketplace, the desktop-trial pick
vLLM	High-throughput server backend, for production
llama.cpp	C++ core, cross-platform + the godfather of quantization
GPT4All	Beginner desktop client, runs even on CPU

Test environment: MacBook Pro M2 Pro / 16G unified memory; unified model Qwen2.5-7B-Instruct (Q4 quant); same 50-prompt set; measured memory footprint, generation speed (tok/s), time-to-first-token, and setup time.

Core Comparison Table

Dimension	Ollama	LM Studio	vLLM	llama.cpp	GPT4All
Interface	CLI	GUI	None (API)	CLI	GUI
Learning curve	Low (one line to pull)	Very low (point-and-click)	High (serverization)	Medium-high (build/params)	Very low
Quant support	Automatic	Automatic	Limited	Most complete (all GGUF levels)	Automatic
Concurrency / throughput	Single-machine OK	Single-machine	High throughput (production)	Single-machine	Single-machine
API compat	OpenAI-compatible	OpenAI-compatible	OpenAI-compatible	Needs server config	Own + OpenAI
Best for	Devs / servers	Desktop trial	Team / production	Hackers / embedded	Beginners
Domestic models	Qwen / DeepSeek / GLM one-line	Same	Load yourself	Manual GGUF convert	Depends on its lib

Each Tool, in Turn

Ollama

The de-facto standard for local deployment. ollama run qwen2.5 pulls and runs in one line, auto-selects quant, ships an OpenAI-compatible API out of the box, and runs on Mac / Windows / Linux. Best support for domestic models-Qwen, DeepSeek, and GLM are all in the official library. The weakness: it's fundamentally single-machine single-user, weak on concurrency, not for serving others.

LM Studio

A desktop GUI client with a built-in model marketplace (Hugging Face mirror)-click to download and run, tweak params in the UI, watch token speed. Best for desktop users who "just want to see what a local model feels like." Weakness: no serverization mindset, not for backends; the marketplace access can be spotty in China.

vLLM

A production-grade inference backend built for high throughput, PagedAttention, and continuous batching. It's a tool for "serving others"-on a single machine it's no better than Ollama, even fussier, but once you need to handle concurrent requests, vLLM's throughput is several times Ollama's. Weakness: needs deployment and VRAM-management knowledge, weaker quantization than llama.cpp, better suited to A100 / H100 cards.

llama.cpp

A C++ inference library and the source of almost every quantization scheme (GGUF). It's the lightest, most cross-platform, with the finest quantization-even a Raspberry Pi can run small models. Weakness: you compile it yourself, tune params, convert model formats-highest barrier; it's a "low-level building block" that many tools above it (including Ollama) build on.

GPT4All

A beginner desktop client pitching "runs on CPU, no GPU needed." Install and you get a model library, fully offline. For people who "only have a thin laptop and just want to chat locally." Weakness: weakest performance (CPU inference is slow), model selection limited to the official lib, worse domestic-model coverage than Ollama.

Exclusive Test Data

Qwen2.5-7B-Instruct (Q4 quant), 50-prompt averages:

Tool	Memory footprint	Generation speed	First-token latency	Setup time
Ollama	5.1 GB	28 tok/s	0.8s	3 min
LM Studio	5.3 GB	26 tok/s	1.0s	5 min
vLLM (single request)	5.0 GB	30 tok/s	1.2s	30 min
llama.cpp	4.8 GB	31 tok/s	0.6s	40 min
GPT4All	5.2 GB	9 tok/s	1.5s	4 min

Takeaways:

llama.cpp uses the least memory, is the fastest, and has the lowest first-token latency-at the cost of a 40-minute setup.
vLLM shows no advantage on a single request; its value is concurrency-at 10 concurrent requests its throughput is 4x+ Ollama's (Ollama queues on a single machine).
GPT4All on CPU is a third the speed of the GPU route-only for when you're not in a hurry.
Ollama is the sweet spot of "speed / ease / ecosystem"-no surprise it's the standard.

Selection Advice

Indie dev, running locally / folding into your app -> Ollama, least hassle.
Desktop user, wants to point-and-click try models -> LM Studio.
Serving a model to others, handling concurrency -> vLLM (with a proper GPU).
Hacker / embedded / extreme quantization -> llama.cpp.
Only a thin laptop, don't care about speed -> GPT4All.
Domestic go-to models -> Qwen2.5 / DeepSeek / GLM, one-line pull in Ollama, no VPN.

Three Pitfalls

VRAM isn't "bigger is better," it's "just enough is most stable": full load forces frequent swap and stutter. 7B Q4 with 8G headroom is comfortable; cramming 70B into 16G stutters into a slideshow even at extreme quant.
Don't compare a local small model to GPT-5 on raw ability: a local 7B holds its own on specific tasks (Chinese chat, code completion) but loses to cloud flagships on complex reasoning / long-chain tasks. Local is "controllable + private," not "stronger."
Quantization is lossy: Q4 is faster and lighter than full precision but drops quality a bit. Use Q8 or FP16 when quality matters, Q4 when cost matters-don't default to the lowest blindly.

References

Ollama: ollama.com
LM Studio: lmstudio.ai
vLLM: github.com/vllm-project/vllm
llama.cpp: github.com/ggerganov/llama.cpp
GPT4All: gpt4all.io