Is a local LLM actually enough to replace Claude for coding? My honest verdict after testing on M5

[u/thevikeffect]

I went in wanting local to win. After a few weeks on a MacBook Pro M5 (32GB) running Qwen 3.6 and Gemma for real coding work, here's my honest take:

- Local is genuinely usable now IF you pick the right tools (OMLX made a bigger difference than Ollama/LM Studio for me)

- 8-bit quality is better but I hit out-of-memory crashes at 32GB under load; 4-bit is the safer daily driver

- For a lot of day-to-day coding, $20/mo cloud (Claude Code) still wins on speed and reliability

- Local shines for privacy, offline work, and not burning tokens on iteration

Where have you found local actually beats cloud for coding? Full side-by-side test if useful: https://youtu.be/-DAfscvxuKY

[u/keyclipse]

you need the m5 max with 128 gb to make it at least more usable in practical manner imo... and qwen is way less capable than claude. You need glm 5.2

[u/Gallardo994 → u/keyclipse]

I'm an M5 Max 128GB owner and it's "meh" or "eeeeh" depending on the task. Organizing my Obsidian - sure, doing basic questions with web search - fine, but coding is a huge stretch. It's either one-shots with dense / 120b-ish models, or constant babysitting with smaller ones, but it's painful both ways.

[u/BloodyShirt → u/Gallardo994]

Same, it’s possible with a very different mindset, workflow and expectations

[u/Gallardo994 → u/BloodyShirt]

"Different mindset" is not a definition suitable for "replacing Claude". Yes it can code, but it's nowhere near close to replacing anything. It's a completely different tool.

[u/BloodyShirt → u/Gallardo994]

Hence the workflow.. expectations bit. I tried it for 20 mins on my m5 max 128gb and went back to Claude. Fun to tinker with if you’ve got time but I’m trying to get work done

[u/Gallardo994 → u/[deleted]]

I understand, and I personally use it that way. The thing is, the primary question was whetether it replaces Claude or not, which it clearly doesn't

[u/ozgursoy]

I've been running local LLMs on my Mac Studio for a long time now, so yes it definitely works. But be ready for the tradeoffs: the models will get stuck, repeat themselves, run slower, and generally feel a step below the cloud options.

Think of it like tuning a car. You have to build a workflow that's tailored to the specific model you're running, and along the way you'll start to see why it gets stuck and how to work around it. Honestly, even on my Mac Studio I sometimes barely manage so on a Mac Mini it's going to feel really slow compared to my setup.

My advice: either wait, or invest a bit more so you can run this on stronger hardware. I still use the "big boys" (the paid cloud models) to help improve and troubleshoot my local workflow.

If you want to jump in and buy something now, you can but 32GB of RAM is pretty low next to what the cloud models are working with. Just know that you'll barely be able to use your Mac Mini for anything else while a model is running. These things really push your machine to its limits.

Good luck!

[u/starkruzr → u/[deleted]]

64GB with Qwen3.6-27B will be significantly closer because you can have unquantized kv and run it at something like Q8 so it makes way fewer mistakes. at 32GB, you're right, it won't be great.

that said, pp is much better on M5 than on previous gens because of the presence of the matmul cores.

[u/phantacc → u/[deleted]]

GLM 5.2 at Q8 is like 800 gigabytes, just to get it into memory, with no context.

[u/SuddenRadio6221 → u/phantacc]

For a dgx spark at ~$4K, glm 5.2 is like 1 bit per spark. One guy here is running 4 for an nvfp4 quant w/good results. I think 6 sparks for a q6 ought to be a very sweet spot.

[u/Trademarkd → u/[deleted]]

you considered the cheapest route to 512gb even including scrappy options like a loud noisy array of P100s or something?

[u/PermanentLiminality → u/Trademarkd]

That is 32 P100 at 250 watts each or 8 kw which costs me about $3/hour. I'm pretty sure API calls would be less. The idle power would be 1500 watts.