Run Local Coding LLMs: A Guide for PC, Mac & RTX 4090

8/11/2025

So, you wanna run a coding LLM on your own machine? Awesome. Honestly, it's a game-changer. No more worrying about privacy, no more paying for APIs, & you can tinker with it to your heart's content. But here's the thing: choosing the right model for your hardware can be a real headache. There are SO many options out there, & what works on a beastly gaming rig will bring a laptop to its knees.

That's what this guide is all about. I'm gonna break it all down for you, from the lightweight models you can run on a potato to the massive ones that'll make your RTX 4090 sweat. We'll cover what to expect, how to get started, & which models are actually worth your time.

The Low-End Warriors: Coding on a Budget PC (4-8GB VRAM)

Got an older machine or a laptop that's not exactly a powerhouse? Don't worry, you can still get in on the local LLM action. The key here is to go for small, lightweight models that have been "quantized." Think of quantization as a way to shrink a model down so it uses less memory, with a small trade-off in accuracy. It's what makes running these things on consumer hardware possible.

Here are some of the best options if you're working with limited VRAM:

Phi-3 Mini (3.8B parameters): This little guy from Microsoft is a real gem. It's surprisingly smart for its size & has some solid reasoning skills. It can run on as little as 4GB of VRAM, making it perfect for older laptops or entry-level PCs. It's a great choice for general chat, light coding tasks, & summarization.
Gemma 2B & 7B (by Google): Google's Gemma models are another excellent choice for low-end hardware. The 2B model is incredibly lightweight & can even run on CPUs if you don't have a dedicated GPU. The 7B version is a bit more capable & is a great all-rounder for coding & chat. Both are licensed for commercial use, which is a nice bonus.
Mistral 7B: This one's a fan favorite for a reason. It's one of the best-performing 7B models out there & is super efficient. You'll need around 8GB of VRAM to run it comfortably, but it's well worth it for the performance you get. It's great for building chatbots, code assistants, & more.
Llama 3.1 8B (Quantized): Meta's Llama 3.1 is a powerhouse, & thanks to quantization, you can run the 8B version on a machine with 8GB of VRAM. It's a fantastic all-purpose model that's great for just about anything you throw at it, from coding to creative writing.

How to get started on a low-end PC:

Your best bet for running these models is to use a tool like Ollama or LM Studio. They make the whole process super easy. With Ollama, you can run a model with a single command in your terminal. LM Studio gives you a nice graphical interface where you can download models, chat with them, & tweak settings. Both are great for beginners.

A quick note on customer service & chatbots: If you're building a customer-facing application, you might be tempted to run a small LLM locally to power a chatbot. While that's possible, you need to think about reliability & uptime. For something like that, you're often better off with a dedicated solution. For instance, Arsturn helps businesses create custom AI chatbots trained on their own data. It's a no-code platform that lets you provide instant customer support & engage with website visitors 24/7, without you having to worry about managing the underlying infrastructure.

The MacBook Crew: Apple Silicon & the Magic of Unified Memory

If you've got a MacBook with an M1, M2, or M3 chip, you're in for a treat. The "unified memory" architecture of Apple Silicon is a HUGE advantage for running LLMs. Basically, the CPU & GPU share the same pool of memory, which makes everything way more efficient. This means you can often run larger models on a Mac than you could on a PC with the same amount of "RAM."

Here's a breakdown of what you can run, based on your Mac's memory:

8GB RAM:

You'll be sticking to the smaller models here, just like on a low-end PC. Think Phi-3 Mini, Qwen 4B, & Gemma 2B. These are great for light tasks, but you'll feel the memory constraints if you push them too hard.

16GB - 18GB RAM:

This is the sweet spot for a lot of developers. You can comfortably run some of the best 7B to 14B models out there.

Llama 3 8B: The go-to all-rounder. Excellent for coding, reasoning, & general chat.
Deepseek Coder v2 (16B-lite): A fantastic coding assistant that punches way above its weight.
WizardLM2 7B: Known for its impressive reasoning skills.

24GB - 36GB RAM:

Now we're getting into some serious power. You can start playing with Mixture-of-Experts (MoE) models, which are super efficient.

Mixtral 8x7B: A classic MoE model that gives you the knowledge of a much larger model with the speed of a smaller one.
Gemma2 27B: Google's 27B model is a performance beast & great for multilingual tasks.
Yi:34B: A strong model with great reasoning abilities.

48GB - 64GB RAM & beyond:

With this much memory, you can run some of the most powerful open-source models available, getting you close to the performance of proprietary APIs like GPT-4.

Llama 3 70B: The gold standard for 70B models. It's a true powerhouse.
Mixtral 8x22B: The bigger brother of the 8x7B model, with even more knowledge.
Deepseek Coder 67B: If you're doing serious, large-scale software engineering, this is the model for you.

How to get started on a Mac:

Ollama is your best friend here. It's a simple command-line tool that makes it incredibly easy to download & run LLMs on your Mac. It handles all the nitty-gritty details for you, so you can get right to the fun part.

If you're looking to integrate a powerful chatbot into your business website, especially if you're on a Mac, you'll want something that's easy to set up & manage. Arsturn is a great option. It's a conversational AI platform that helps businesses build meaningful connections with their audience through personalized chatbots. You can train it on your own data, so it can answer specific questions about your products or services, which is pretty cool.

The High-End Heroes: Pushing the Limits with an RTX 4090

If you've got a beast of a machine with an RTX 4090, you can really let loose. With 24GB of VRAM, you can run some seriously large & powerful models. Here's what you can expect:

Running the big boys: You can comfortably run 32B parameter models like Qwen 2.5 Coder 32B. This model is a coding beast & can handle complex tasks with ease. Some users have even managed to run quantized versions of 70B models, though it's a tight squeeze.
Quantization is still your friend: Even with 24GB of VRAM, you'll still be using quantized models for the larger parameter counts. A Q4 or Q5 quantization is a good starting point, offering a nice balance of performance & accuracy. You can experiment with higher quantization levels like Q8, but you might find that the model becomes too slow to be useful.
Desktop vs. Laptop RTX 4090: This is a BIG one. A desktop RTX 4090 is significantly more powerful than the laptop version. The desktop card has more CUDA cores & a higher power draw, which translates to much better performance, especially with larger models. A 32B parameter model might be unusable on a laptop 4090 but perfectly fine on a desktop one.
Multiple GPUs: If you're REALLY serious, you can even run multiple GPUs. With two RTX 3090s (which have a similar amount of VRAM to a 4090), you can run a 32B model at a respectable speed. With a setup like that, you can start to rival the performance of cloud-based solutions.

How to get started on a high-end PC:

Just like with the other tiers, Ollama & LM Studio are great starting points. If you want more fine-grained control, you can also look into tools like koboldcpp. For the more adventurous, setting up a multi-GPU environment with a tool like vLLM can unlock some serious performance.

A thought on business automation: When you have this much power at your fingertips, you start to think about how you can automate things. For businesses, this is where things get really interesting. Imagine having a local LLM that can analyze sales data, generate reports, & even write marketing copy. While you can build something like that yourself, it's a lot of work. That's where solutions like Arsturn come in. It helps businesses build no-code AI chatbots trained on their own data to boost conversions & provide personalized customer experiences. It's a great example of how you can leverage the power of AI without having to become an expert in managing complex models & infrastructure.

The Takeaway

Running a coding LLM locally is an incredibly rewarding experience. It gives you a level of freedom & control that you just can't get with cloud-based solutions. Whether you're on a budget laptop or a high-end gaming rig, there's a model out there for you.

The key is to understand the limitations of your hardware & to choose a model that's a good fit. Don't be afraid to experiment with different models & quantization levels to find what works best for you. And remember, the local LLM scene is moving FAST, so what's cutting-edge today might be old news tomorrow.

I hope this guide was helpful! Let me know what you think, & happy coding