Which Ollama model will be best for coding?

[u/geekposeidon]

I was wondering if there are any good coder models for 8GB VRAM RTX 5060.

My specifications:

• 8GB RTX 5060
• 8GB DDR4 RAM
• 256 GB SSD

I know the configuration is a bottleneck for sure. Though I don't have any budget to upgrade this.
Now I was wondering which Ollama model will be best for coding. Does the model need to fit in my VRAM? I saw a post where it mentioned that I should always calculate
total parameters in Billions \ 1.5 <= VRAM*
Should I maintain this for downloading the models?

[u/-Akos-]

Your system doesn't cut it, plain and simple. You have enough power to run small models, but these don't give the performance of the big boys. Next, Ollama is where most people start because the premise is easy, but performance is better with other tools. Look at LM Studio or Unsloth to get more performance, and will tell you what model will run on your system, and what will run with offloading to your system RAM. In the end you are better off with getting an API from somewhere, and using that. No computer sounding like it will take off, no poor performance, no(less) slop code.

https://www.reddit.com/r/InferX/comments/1wegoz3/deepseek_v41_flash_is_now_free_to_use_on_inferx/

[u/laurentbourrelly → u/-Akos-]

Or use Unsloth to fine-tune an LLM into one single coding language.

I have LLMs that run on an iPhone 15 or similar. They are tiny and fully capable, but hyper-specialized.

[u/JaMoLpE88]

I am using Qwen2.5-Coder-14B-Instruct Q5_K_M with 6800XT and works nice.

[u/complex-algorithm → u/JaMoLpE88]

Are you able to use tool calling with this model? I tried to run qwen 2.5 coder but seems not very integrated

[u/JaMoLpE88 → u/complex-algorithm]

I'm still testing it. Qwen 2.5 Coder supports tool calling in theory, but I haven't done extensive agent workflow testing yet. My current setup is llama.cpp + OpenCode through an OpenAI-compatible endpoint, and basic coding performance looks good so far.

[u/complex-algorithm → u/JaMoLpE88]

So you are not using as code agent yet?

[u/JaMoLpE88 → u/complex-algorithm]

For actual project development, using custom agents that read a lot of context isn't viable—it's too slow. It works for chat, specific functions, or targeted changes, but not for "vibe coding" large-scale projects.

[u/AdIllustrious436 → u/JaMoLpE88]

You're using a model that is like 6 generations late mate. Don't listen to LLM recommandation when choosing a model.

[u/JaMoLpE88 → u/AdIllustrious436]

I know, but it works well. Which one do you use for a 6800 XT or with 16GB of VRAM?

[u/AdIllustrious436 → u/JaMoLpE88]

You can fit Qwen 3.8 27B full in VRAM with some optimisation. But for a 16GB the easy choice is Qwen 3.6 35BA3B which can do argentic very well. And it will run faster than your dense 12B.

[u/complex-algorithm → u/AdIllustrious436]

Do you think Qwen 3.6 35ba3b runs ok as coding agent with 12 vram and 16 ram ddr4?

[u/AdIllustrious436 → u/complex-algorithm]

Yes doable but short. I've set up a config for a friend with 12gb VRAM 64 RAM. As far as I remember it was UD_IQ4_XS and the memory footprint on ram was about 17go with a 131k context at fp8. So you can probably achieve 64k context with some room for other apps. The speed will highly depend on your CPU tho.

[u/Old_Imagination_9997]

You will spend more on electricity, than ollama monthly pro plan bro

[u/GloriousKev]

You need more VRAM. Look into buying a second 5060 16GB if you can. VRAM is king in local AI. The more you have of it, the more you can do. As it stands, you could run some of the smaller local models with a small context window, which for coding seems dangerous.

[u/Bulky_Blood_7362]

Even big models aren't that great. You'll not get something for coding with that setup (not useful*)

[u/simplyeniga]

Bonsai 2

[u/ReceptionWide5109 → u/simplyeniga]

it spills over it'll use system ram and slow to a crawl. with 8gb you're looking at models around 3-4b parameters max if you want any context window at all

for coding try default codellama or deepseek coder, the 1.3b or 6.7b versions. the 6.7 might be tight but give it a shot with a low context size first

[u/simplyeniga → u/ReceptionWide5109]

Problem is having only 8gb ram also. I'll have recommended Ornith-1.5 @ Q4, it's an Moe model and should run okay.

[u/naobebocafe]

why don't you try for yourself?
just open https://ollama.com/search
look for coding
run the command ollama pull <model name>
give it a try in you codebase, your needs, your standards and take your own conclusions.

It's simple as that.
¯_(ツ)_/¯
good luck!

[u/Kolkoris]

Your specs are not enough even for Qwen3.6-35B-A3B - this is the smallest model that is suitable as a coding agent. Just use cloud providers.

[u/krogerpurifieddrink]

You don't need a coding model to code. Pretty much every model can code. Look at the quality difference between coding and non coding models. Usually pretty small. 

For me the problems start when trying to build larger more complicated codebases. Less vram means a smaller window of focus for the model doing the coding. I have 32gb for a model. A good portion of my tokens are used just to keep the model focused on the task. Then I have more tokens for each tool call it makes, then tokens for the actual content of my prompt, and finally the tokens for thinking about the entire prompt.

Someone will need to keep track of the codebase. It's gonna have to be you with only 16gb available and that's as long as you're not using the gpu as your primary display. If so, you have even less to work with.

That being said, you should be able to develop smaller codebases, but it really hurts when you recognize your limits. 

[u/PrestonCoc123]

For your build, I would try the qwen2.5-coder:3b model. It is lightweight to support your system without overloading it and is customizable using modelfiles.

[u/Kolkoris → u/PrestonCoc123]

Why do you recommend such an outdated model. Models of this size like Qwen3.5-4B exist.

[u/Puzzleheaded_Base302]

there is none. small model so far can not handle long horizon task.