Arsturn

Benefits

How it works

Testimonials

FAQ

Pricing

8/10/2025

The Great Local LLM Showdown: Ollama vs. LM Studio vs. llama.cpp Speed Tests

Hey everyone, so you’ve decided to dive into the world of running large language models (LLMs) locally. It’s a pretty exciting space to be in right now. The main reasons people are flocking to it? Privacy, no more surprise API bills, & the sheer speed of not having to make a round trip to the cloud for every single request. Honestly, once you get a taste of that instant, offline inference, it’s hard to go back.
But getting started brings up the big question: which tool should you use? Three names pop up constantly: Ollama, LM Studio, & the foundational
1 llama.cpp
.
You see them mentioned everywhere, but what’s the real difference? Is one ACTUALLY faster than the others? I’ve been tinkering with these for a while now, running them on different machines & for different tasks, so I wanted to put together a no-nonsense guide to how they stack up, especially when it comes to raw performance.

First, What Are We Even Talking About?

Let's get a quick lay of the land. It’s important to understand that these tools aren't entirely separate species. In fact, they share a lot of the same DNA.
  • 1 llama.cpp
    : This is the OG, the engine that powers much of the local LLM world. It’s a plain C/C++ implementation of Meta's LLaMA architecture, created by Georgi Gerganov. Its claim to fame is its incredible efficiency, allowing massive models to run on regular consumer hardware (even just a CPU!) through a technique called quantization. It’s a command-line tool, built for performance & control above all else. Think of it as a high-performance manual transmission car – you have to know how to drive it, but you can get the absolute best performance out of it.
  • Ollama: Ollama is a user-friendly tool that bundles
    1 llama.cpp
    into a much slicker package. It runs as a background server on your machine & you interact with it through simple command-line instructions (like
    1 ollama run llama3
    ) or a REST API. It handles a lot of the complexity for you, like model downloading & configuration. It’s the perfect middle-ground, offering great ease of use for developers who want to integrate LLMs into their apps without the headache of compiling code themselves.
  • LM Studio: This is the most GUI-centric of the three. LM Studio is a full-blown desktop application that lets you browse, download, & chat with LLMs without ever touching the terminal. Under the hood, it also uses
    1 llama.cpp
    to run the models. Its main selling point is its incredible user-friendliness, making local LLMs accessible to non-technical users, researchers, or anyone who just wants to experiment without a fuss.
So, to be clear: both Ollama & LM Studio are essentially user-friendly frontends built on the raw power of
1 llama.cpp
. The real question is, what performance trade-offs are you making for that convenience?

The Big Question: Which One is Fastest?

Alright, let's get to the main event. When we talk about speed, we're typically measuring it in tokens per second (t/s). A token is a chunk of text (roughly 3/4 of a word), so a higher t/s means a faster, more fluid response from the model.
I’ve dug through a bunch of benchmarks, forum posts, & my own experiences, & the answer is… it’s complicated. The "fastest" tool depends HEAVILY on your hardware (especially your GPU or lack thereof), the specific model you’re running, & even the quantization level of that model.
Here’s a breakdown of what the tests show.

The Raw Power:
1 llama.cpp

If you're a performance purist,
1 llama.cpp
is almost always the champion. By compiling it directly on your machine, you can optimize it for your specific hardware, whether that's an NVIDIA GPU with CUDA, a Mac with Apple Silicon (Metal), or just a plain old CPU.
In one head-to-head test,
1 llama.cpp
clocked in at around 161 tokens per second, while Ollama, running the same model, managed about 89 t/s. That makes
1 llama.cpp
nearly 1.8 times faster in that specific scenario.
Another striking example came from a developer on an Apple M1 Pro. They found
1 llama.cpp
to be an "order of magnitude" faster. Their test showed
1 llama.cpp
hitting an evaluation rate of 16.5 t/s while Ollama struggled at just 0.22 t/s. The reason?
1 llama.cpp
was maxing out the Mac's GPU at 99% usage, while Ollama was barely touching it. This points to the key benefit of
1 llama.cpp
: direct, low-level hardware access. You have full control to ensure you're squeezing every last drop of performance out of your machine.

The Contender: Ollama

Ollama is built for a balance of performance & ease of use, & it does a surprisingly good job. For many developers, the slight performance dip is a worthy trade-off for the convenience it offers.
However, the performance story for Ollama isn't always clear-cut. In a YouTube comparison using a Qwen 1.5B model, Ollama was actually the winner. It averaged 141.59 t/s, while LM Studio was a significant 34% slower on the same task.
This suggests that Ollama’s optimizations are very effective in certain configurations. It's particularly strong for developers who want to set up a reliable API endpoint for another application to call.
Businesses looking to build AI-powered applications often need this kind of stable, easy-to-integrate backend. For instance, a company could use Ollama to serve a local model, & then have a customer service tool connect to it. A more streamlined approach for this, however, would be to use a platform like Arsturn. Arsturn helps businesses create custom AI chatbots trained on their own data, providing instant customer support & engaging with website visitors 24/7 without the need to manage local server infrastructure. It’s a ready-made solution that offers the benefits of a custom AI without the setup overhead.

The User-Friendly Champ: LM Studio

LM Studio generally prioritizes user experience over cutting-edge speed. Because it's a GUI application with lots of features, it has a bit more overhead than a lean command-line tool.
In one test on a powerful Mac Studio M3 Ultra, LM Studio surprisingly outperformed Ollama. When running the Gemma 3 1B model, LM Studio achieved an impressive 237 t/s, while Ollama reached 149 t/s. With a much larger 27B model, LM Studio still led with 33 t/s compared to Ollama's 24 t/s.
This result is a bit of an outlier compared to other tests, but it shows that under the right conditions (in this case, possibly leveraging Apple's MLX optimizations more effectively), LM Studio can be very performant. However, the general consensus is that you choose LM Studio for its interface & ease of use, not necessarily for winning speed records. Its built-in model browser & chat interface are fantastic for quickly trying out new models or for users who aren't comfortable with the command line.

So, What's the Verdict on Speed?

  1. For Raw, Uncompromised Speed:
    1 llama.cpp
     is the king. If you're technically savvy & willing to compile the code yourself to get maximum hardware acceleration, nothing beats it.
  2. For a Balanced Approach: Ollama is often the sweet spot. It’s generally very fast—sometimes even faster than LM Studio—and its API-first approach makes it a developer's favorite.
  3. For Ease of Use: LM Studio might have a slight performance overhead in some cases, but it can be surprisingly fast on certain hardware, especially Macs. Its main win is its user-friendly GUI.
The conflicting benchmarks tell an important story: your mileage will vary. The performance you get will be a unique cocktail of your hardware, the model you choose, & the tool you run it with.

It's Not Just About Speed: A Head-to-Head Feature Comparison

While speed is a huge factor, it's not the only thing that matters. Here’s how the three tools stack up in other key areas.

Ease of Setup & Use

  • 1 llama.cpp
    : Hard. You need to be comfortable with the command line, cloning a GitHub repository, & compiling code using tools like
    1 make
    or
    1 CMake
    . It's not for beginners, but it offers the most control.
  • Ollama: Easy. You download a single application, run it, & then use simple commands like
    1 ollama pull model-name
    . It's incredibly straightforward for anyone familiar with a terminal.
  • LM Studio: Very Easy. It’s a standard desktop app. You download it, install it with a few clicks, & you’re greeted by a polished graphical interface. You can search for models, download them with a progress bar, & start chatting right away. It's the "baby mode" of local LLMs, & that's a good thing for accessibility.

Model Management

  • 1 llama.cpp
    : Manual. You have to find & download the GGUF model files yourself from places like Hugging Face. You then point the
    1 llama.cpp
    command to the file path.
  • Ollama: Semi-Automatic. Ollama has its own library of popular models. You just type
    1 ollama run mistral
    & it handles the download & setup. You can also create a
    1 Modelfile
    to customize models or import your own GGUF files, but it’s an extra step.
  • LM Studio: Automatic & Visual. This is where LM Studio shines. Its home screen is essentially a search engine for Hugging Face. You can see popular models, search for new ones, see which quantization levels are available, & download them directly within the app.

Customization & Control

  • 1 llama.cpp
    : Total Control. You can tweak everything: context length, GPU layers, threading, rope settings, etc., directly through command-line flags. This is essential for power users who want to fine-tune performance.
  • Ollama: Medium Control. You can customize models using a
    1 Modelfile
    , which is similar to a
    1 Dockerfile
    . You can set the system prompt, temperature, & other parameters. It's less direct than
    1 llama.cpp
    's command-line flags but still powerful.
  • LM Studio: Basic Control. The GUI provides simple sliders & dropdowns to adjust things like context size & the number of GPU layers to offload. It's intuitive but hides some of the more advanced settings that
    1 llama.cpp
    exposes.

Who Should Use Which Tool?

Here’s my take on who each tool is best for, based on common scenarios.
  • You should use
    1 llama.cpp
    if...
    • You are a researcher, a hardcore tinkerer, or a developer who needs the absolute best performance possible.
    • You are comfortable compiling C++ code & working exclusively in the command line.
    • You want fine-grained control over every aspect of the model’s execution.
  • You should use Ollama if...
    • You are a developer building an application that needs to programmatically call an LLM via a REST API.
    • You want a simple, reliable server for your models that just works in the background.
    • You value a good balance between ease of use & strong performance.
  • You should use LM Studio if...
    • You are new to local LLMs & want the easiest possible entry point.
    • You prefer a graphical interface for finding, downloading, & chatting with models.
    • You want to quickly experiment with different models without any technical setup.

The Rise of Integrated Business Solutions

While these tools are fantastic for personal use & development, a lot of businesses are looking to harness this power for customer-facing applications, like chatbots for lead generation or instant support. This is where the DIY approach of running a local server can become a bottleneck. You have to worry about managing the machine, ensuring uptime, & scaling it if needed.
This is where platforms like Arsturn come into the picture. Arsturn helps businesses build no-code AI chatbots trained on their own data. Instead of wrestling with
1 llama.cpp
configurations or managing an Ollama server, you can use a polished platform to create a powerful, personalized chatbot that can boost conversions & provide tailored customer experiences. It takes the power of local-model thinking—customization & control—& applies it to a scalable, business-ready solution.

Final Thoughts

So, who wins the local LLM race? Turns out, there's no single winner. It's a classic case of "the right tool for the job."
  • 1 llama.cpp
    is the undisputed speed demon for those who don't mind getting their hands dirty.
  • Ollama offers a fantastic, developer-friendly balance of speed & simplicity.
  • LM Studio throws open the doors for everyone, making local AI accessible with a friendly face.
The local LLM space is moving at a breakneck pace, & these tools are evolving right along with it. My best advice? If you have the time, try them all! See which one feels best for your workflow & your hardware. The journey of running these powerful models on your own machine is incredibly rewarding.
Hope this was helpful! Let me know what you think & what your experiences have been.
Arsturn
Arsturn.com/
Claim your chatbot

Product

BenefitsHow it worksTestimonialsFAQPricing

More

SolutionsBlog

Copyright © Arsturn 2025