Best Local AI Models for Math Problems: A Showdown

8/10/2025

The Ultimate Showdown: Which Local AI Model is Best for Cracking Math Problems?

Hey everyone, let's talk about something that's been a hot topic in the AI space lately: running seriously powerful AI models for math, but on your own machine. For a long time, if you wanted top-tier AI, you had to rely on the big cloud players. But now, with the explosion of open-source & open-weight models, it's a whole new ball game. We're talking about having your own private math genius, right on your desktop.

But here's the thing: with so many models popping up, how do you know which one is actually any good? And more importantly, what kind of hardware do you need to run them without your computer melting? I've been digging into this, & honestly, the results are pretty exciting. We're going to break it all down – from the massive, newly released models to the smaller, highly specialized ones.

The Big Players Go Local: A New Era of Open-Weight Models

You've probably heard of the big names like GPT-4, Claude, & Gemini. They're amazing, but they've always been behind a corporate wall. That's starting to change.

Just recently, OpenAI dropped a bombshell by releasing open-weight versions of some of their models, specifically GPT-OSS 120B & 20B. This is a HUGE deal. These models are designed to be run locally, & they're particularly strong in logical reasoning & mathematical problem-solving. The 120B model is a beast, meant for high-performance systems, while the 20B version is more accessible for everyday desktops. Early benchmarks show that the gpt-oss-120b can even outperform some of the proprietary models on math competition benchmarks like AIME.

This move by OpenAI is a game-changer because it allows developers & researchers to really get under the hood, customize the models, & use them for all sorts of applications without being tied to an API. It's a massive step towards democratizing powerful AI.

The Rise of the Math Specialists: Smaller, Focused Models

While the big models are great all-rounders, a new breed of smaller, highly specialized models is emerging, & they are laser-focused on one thing: math. These are the models that are getting a lot of attention in the local AI community, & for good reason.

DeepScaleR-1.5B: The Olympiad Champion

One of the most talked-about models right now is DeepScaleR-1.5B. This little powerhouse is specifically fine-tuned on around 40,000 problems from math competitions like the American Invitational Mathematics Examination (AIME) & the American Mathematics Competition (AMC). Think of it as an AI that has spent its entire life training for math olympiads.

What's really impressive is that DeepScaleR-1.5B, with only 1.5 billion parameters, is punching way above its weight. It's been shown to achieve a 43.1% Pass@1 accuracy on the AIME 2024 benchmark, which is a significant improvement over its base model. It's also showing strong performance on other benchmarks like MATH 500. The best part? It's small enough to be run on a decent consumer-grade GPU.

MathCoder2 Llama-3 8B: The Code-Driven Mathematician

Another fascinating model is MathCoder2 Llama-3 8B. This one takes a slightly different approach. The researchers behind it found that training a model on mathematical code—think Python scripts that solve math problems—can significantly boost its reasoning capabilities. So, they created a massive dataset of mathematical code paired with reasoning steps & used it to continue pre-training a Llama-3 8B model.

The results speak for themselves. MathCoder2-Llama-3-8B achieves a 69.9% accuracy on the GSM8K benchmark & 38.4% on the more challenging MATH benchmark. This is a noticeable improvement over the base Llama-3 8B model, which is already very capable. The idea here is that the logic & precision of code help the model to "think" more like a mathematician.

Let's Talk Benchmarks: How Do They Stack Up?

Okay, so we have these different models, but how do they actually compare? This is where benchmarks like GSM8K & MATH come in.

GSM8K (Grade School Math 8K): This is a dataset of thousands of grade school math word problems. They're designed to be conceptually simple but require multiple steps to solve. It's a great test of a model's basic reasoning abilities.
MATH: This is a much harder benchmark, consisting of 12,500 problems from high school math competitions. This is where the real math whizzes, both human & AI, are put to the test.

Here's a rough idea of how some of these models perform:

Model	GSM8K (Accuracy)	MATH (Accuracy)
GPT-4 (for context)	~92% (5-shot)	~61%
GPT-OSS 120B	Strong, competitive with top models	Outperforms some on AIME
MathCoder2 Llama-3 8B	69.9% (4-shot)	38.4% (4-shot)
DeepScaleR-1.5B	-	Strong on MATH 500 & AIME
Llama-3 8B (base)	~79.6% (8-shot)	~30%

Note: Benchmark scores can vary based on the prompting method (e.g., zero-shot, few-shot) & other factors. These are approximate figures to give a general idea.

What's really interesting here is that the specialized models, while not always reaching the raw power of something like GPT-4, are showing INCREDIBLE performance for their size. A model like MathCoder2 Llama-3 8B, which you can run on a good gaming PC, is getting pretty close to the GSM8K performance of much larger models.

The Elephant in the Room: Hardware Requirements

This is the big question for anyone wanting to run these models locally. The truth is, it all comes down to VRAM (the memory on your graphics card). The model's parameters need to be loaded into VRAM to run efficiently.

Here's a general guide:

For smaller models (like 1.5B to 8B): You'll want a GPU with at least 12GB of VRAM. An NVIDIA RTX 3060 (12GB version), 3080, 4070, or a used 3090 would be a great starting point. With these, you can comfortably run models like DeepScaleR-1.5B & MathCoder2 Llama-3 8B.
For larger models (like 20B and up): You're going to need more firepower. A GPU with 24GB of VRAM like an RTX 3090 or 4090 is pretty much essential. For the REALLY big models, you'd be looking at workstation or server-grade hardware with multiple GPUs.

But here's a pro-tip: quantization. This is a process that reduces the precision of the model's parameters, making them take up less space. For example, a 16-bit model can be quantized to 8-bit or even 4-bit, roughly halving the VRAM requirement each time. This can be a game-changer, allowing you to run larger models on less powerful hardware, though there might be a small trade-off in performance.

Practical Applications & Getting Started

So, what can you actually DO with a local AI math model? The possibilities are pretty cool.

Students & Educators: Imagine having a private math tutor that can explain concepts, walk you through problems, & generate practice questions on demand.
Researchers & Engineers: These models can be powerful assistants for solving complex equations, verifying proofs, & even helping to write scientific code.
Businesses: For businesses that deal with a lot of data analysis, financial modeling, or engineering calculations, having a local AI can be a secure & cost-effective way to get answers without sending sensitive data to the cloud.

And here's where things get REALLY interesting for businesses. Imagine you have a website that sells a technical product or offers a complex service. You could use a platform like Arsturn to build a custom AI chatbot trained on your own data, including the documentation for your product & the kinds of math problems your customers might have. With Arsturn, you can create a no-code AI chatbot that provides instant, 24/7 support to your website visitors. It could answer their questions, guide them through complex calculations, & even help them choose the right product based on their needs. It's a fantastic way to boost engagement & provide a truly personalized customer experience.

So, Which Model Should You Choose?

Here's my take on it:

If you're a student or hobbyist with a decent gaming PC (12GB+ VRAM): Start with MathCoder2 Llama-3 8B. It's a great all-rounder with strong performance on common math benchmarks.
If you're serious about competitive math or advanced problem-solving: DeepScaleR-1.5B is an amazing choice. It's highly specialized & punches well above its weight.
If you have a powerful workstation (24GB+ VRAM) & want to experiment with the cutting edge: Try out the GPT-OSS 20B model. It's a great way to experience the power of a large open-weight model.

The world of local AI is moving at an incredible pace, & it's so exciting to see these powerful tools become more accessible. The ability to run a sophisticated math AI on your own machine opens up a world of possibilities.

Hope this was helpful! I'd love to hear what you think, & if you've tried running any of these models yourself. Let me know in the comments