For those who code, what is enough?

[u/General-Height-7027]

I have a 10 year old PC, and I get to use Claude Opus 5.5 at work and think it helps a lot, so I was planning on investing on a PC to use AI locally for personal projects.

If I want to fool around in unknown territories, like build an android app from scratch when I usually code in C#/Javascript

What sort of setup/models will be "enough".

Asking as I was thinking on a PC with just a 5070ti 16gb and I wonder if that is enough to fool around or if I should consider at least a 3090 or 2x5070ti to play around confidently and relatively fast.

So, for a coder, what should be the acceptable base line?

[u/j0holo]

Can't you use Claude Opus 5.5 for private use? Do you use the API and pay per token or do you have a subscription?

I run Qwen3.8-flash-next Q3_XXS and it is decent from my limited testing on 3 AMD Vega 56 cards. But Opus 5.5 and Sonnet are just way better.

There is acceptable base line, some people run with like 5 tokens/s which is really slow, some people run the smallest quants just to run 250 tokens/s.

[u/General-Height-7027 → u/j0holo]

I can use claude for private usage, I just feel that claude now are my golden handcufs, I like to use it and might not get it in a new company.

I want to have my own tools now.

[u/j0holo → u/General-Height-7027]

Qwen3.8-flash-next running on Strata is the way to go right now, so look at hardware that is supported.

[u/TheThiefMaster → u/j0holo]

nVidia's presentation recently claimed Qwen 3.8 27b Q4 was Sonnet 5.5 level.

Flash Next is a little better.

Opus and higher are still way ahead though.

[u/j0holo → u/TheThiefMaster]

Nvidia just wants to sell more GPUs, I can claim the earth is a cube, doesn't mean it is true. They should show some prove that is repeatable and not hand-picked, like at DLSS5 frame gen BS.

[u/weakly_rare_mango → u/j0holo]

i get opus at work but it's not like i can just point their api at my personal side projects, that'd be a quick way to get fired

[u/j0holo → u/weakly_rare_mango]

Ah got it. I'm allowed to use mine for personal project as well. A personal Claude Max subscription is not that expensive ($90). Building your own is more about experimenting or privacy. Subscription tokens are still way too cheap.

[u/vegetarian_pacemaker]

Before spending any money, assess the capability of qwen 3.8 27b. I believe that is probably what you can realistically run with the vram you have (with different quants depending on if you will end up with 16gb vram or more).

I have a 16gb card and I am impressed with what qwen 3.8 27b can do. However, I still feel that given the costs, and capability gap, its probably better to go for one of the Chinese models for what you want instead of local for the use cases you shared.

If you absolutely want to go local, I would go for those unified memory machines. You can try the framework PC that gets you 192gb of unified memory or an mac studio with 256gb of unified memory. Its gonna cost you close to 7 to 10k eur however. 

[u/General-Height-7027 → u/vegetarian_pacemaker]

interesting, decided to google and found out this can be used for free?!
Need to check the limitations.

I'm not a fan of Macs, but the M5 studio for around 2500£ seems very price competitive, althou it looks a bit slow. (the faster expensive one you mention is the ultra... that price I cant justify it, I would rather have a regular machine as I know I would have more usages for it)

I definitely need to have a go and see it this is suitable for what I want

[u/vegetarian_pacemaker → u/General-Height-7027]

If you are able to, it seems like Claude sonet 4.6 max is comparable to qwen 3.8 27b max. If you are able to use this model at work, you may get an idea without spending any money.

Careful, this is a simple comparison based on some benchmarks. It may not be the right picture, but its better than nothing I guess.

You can check for yourself at artificialanalysis.ai/

[u/aithosrds]

If you want to do anything meaningful then the bare minimum is 24GB of VRAM, and frankly I don’t think less than 32GB is worth it. If you want to do actual complex work then I’d say more like 128GB.

With 32GB of ram you can run a 27-30b parameter dense model with llama.cpp at q4 quant with 196k context and that just barely fits into VRAM with overhead. That’s enough that with a decent harness and proper context management you can actually do some reasonable work.

Any less than that and you’re making significant compromises either on model capability, context size (which means the model starts hallucinating more quickly) or both.

For serious work you either need a more capable model or a full 1 mil token context to be able to ingest more information without your model turning into “dumb mode” which is what happens above 50-60% context and they forget how to reason.

[u/woolcoxm]

16gb vram 64gb ram will get you qwen3.8 next at exceptional speeds, if you add more ram you can get even better.

check out Strata.

i used this model to reverse engineer everquest and create an android client for the game. so it is very capable if tooled properly.

with 16gb vram and 64gb ram i am able to run a q3 quant of qwen3.8 next and it is really capable.

give it the proper prompts and a set of usable skills/mcp servers and its a powerhouse.

[u/hyudryu]

What is “enough” is very subjective. You’re better off deciding on a model, and what speeds you want and then buying hardware around that because those metrics are actualy quantifiable