From a strategic standpoint of privacy, cost and control, I immediately went for local models, because that allowed to baseline tradeoffs and it also made it easier to understand where vendor lock-in could happen, or not get too narrow in perspective (e.g. llama.cpp/open router depending on local/cloud [1] ).
With the explosion of popularity of CLI tools (claude/continue/codex/kiro/etc) it still makes sense to be able to do the same, even if you can use several strategies to subsidize your cloud costs (being aware of the lack of privacy tradeoffs).
I would absolutely pitch that and evals as one small practice that will have compounding value for any "automation" you want to design in the future, because at some point you'll care about cost, risks, accuracy and regressions.
[1] - https://alexhans.github.io/posts/aider-with-open-router.html
And the probability that any given model you use today is the same as what you use tomorrow is doubly doubtful:
1. The model itself will change as they try to improve the cost-per-test improves. This will necessarily make your expectations non-deterministic.
2. The "harness" around that model will change as business-cost is tightened and the amount of context around the model is changed to improve the business case which generates the most money.
Then there's the "cataclysmic" lockout cost where you accidently use the wrong tool that gets you locked out of the entire ecosystem and you are black listed, like a gambler in vegas who figures out how to count cards and it works until the house's accountant identifies you as a non-negligible customer cost.
It's akin to anti-union arguments where everyone "buying" into the cloud AI circus thinks they're going to strike gold and completely ignores the fact that very few will and if they really wanted a better world and more control, they'd unionize and limit their illusions of grandeur. It should be an easy argument to make, but we're seeing about 1/3 of the population are extremely susceptible to greed based illusions.,
Most Anti-Union arguments I have heard have been about them charging too much in dues, union leadership cozying up to management, and them acting like organized crime doing things like smashing windows of non-union jobs. I have never heard anyone be against unions because they thought they would make it rich on their own.
The rest of your points are why I mentioned AI evals and regressions. I share your sentiment. I've pitched it in the past as "We can’t compare what we can’t measure" and "Can I trust this to run on its own?" and how automation requires intent and understanding your risk profile. None of this is new for anyone who has designed software with sufficient impact in the past, of course.
Since you're interested in combating non-determinism, I wonder if you've reached the same conclusion of reducing the spaces where it can occur and compound making the "LLM" parts as minimal as possible between solid deterministic and well-tested building blocks (e.g. https://alexhans.github.io/posts/series/evals/error-compound... ).
I also highly suggest OpenCode. You'll get the same Claude Code vibe.
If your computer is not beefy enough to run them locally, Synthetic is a bless when it comes to providing these models, their team is responsive, no downtime or any issue for the last 6 months.
Full list of models provided : https://dev.synthetic.new/docs/api/models
Referal link if you're interested in trying it for free, and discount for the first month : https://synthetic.new/?referral=kwjqga9QYoUgpZV
The one I mentioned called continue.dev [1] is easy to try out and see if it meets your needs.
Hitting local models with it should be very easy (it calls APIs at a specific port)
For a full claude code replacement I'd go with opencode instead, but good models for that are something you run in your company's basement, not at home
tldr; `ollama launch claude`
glm-4.7-flash is a nice local model for this sort of thing if you have a machine that can run it
I set up a bot on 4claw and although it’s kinda slow, it took twenty minutes to load 3 subs and 5 posts from each then comment on interesting ones.
It actually managed to correctly use the api via curl though at one point it got a little stuck as it didn’t escape its json.
I’m going to run it for a few days but very impressed so for for such a small model.
1. Switch to extra usage, which can be increased on the Claude usage page: https://claude.ai/settings/usage
2. Logout and Switch to API tokens (using the ANTHROPIC_API_KEY environment variable) instead of a Claude Pro subscription. Credits can be increased on the Anthropic API console page: https://platform.claude.com/settings/keys
3. Add a second 20$/month account if this happens frequently, before considering a Max account.
4. Not a native option: If you have a ChatGPT Plus or Pro account, Codex is surprisingly just as good and comes with a much higher quota.
Personally, I’ve used AWS Bedrock as the fallback when my plan runs out, and that seems to work well in my experience. I believe you can now connect to Azure as well.
When you fall back to a local model for coding, you lose whatever safety guardrails the hosted model has. Claude's hosted version has alignment training that catches some dangerous patterns (like generating code that exfiltrates env vars or writes overly permissive IAM policies). A local Llama or Mistral running raw won't have those same checks.
For side projects this probably doesn't matter. But if your Claude Code workflow involves writing auth flows, handling secrets, or touching production infra, the model you fall back to matters a lot. The generated code might be syntactically fine but miss security patterns that the larger model would catch.
Not saying don't do it - just worth being aware that "equivalent code generation" doesn't mean "equivalent security posture."
We've seen some absolutely glaring security issues with vibe-coded apps / websites that did use Claude (most recently Moltbook).
No matter whether you're vibe coding with frontier models or local ones, you simply cannot rely on the model knowing what it is doing. Frankly, if you rely on the model's alignment training for writing secure authentication flows, you are doing it wrong. Claude Opus or Qwen3 Coder Next isn't responsible if you ship insecure code - you are.
I agree nobody should rely on model alignment for security. My argument isn't "Claude is secure and local models aren't" - it's that the gap between what the model produces and what a human reviews narrows when the model at least flags obvious issues. Worse model = more surface area for things to slip through unreviewed.
But your core point stands: the responsibility is on you regardless of what model you use. The toolchain around the model matters more than the model itself.
Obviously it must be assumed that the model one falls back on is good enough - including security alignment.
Not saying that's wrong, just that it's a gap worth being aware of.
For side projects I'd probably agree with you. For anything touching production with customer data, I want both - local execution AND a model that won't silently produce insecure patterns.
My point was narrower than it came across: when you swap from a bigger model to a smaller local one mid-session, you lose whatever safety checks the bigger one happened to catch. Not that the bigger one catches everything - clearly it doesn't.
This is, however, a major improvement from ~6 months ago when even a single token `hi` from an agentic CLI could take >3 minutes to generate a response. I suspect the parallel processing of LMStudio 0.4.x and some better tuning of the initial context payload is responsible.
6 months from now, who knows?
Closed models are specifically tuned with tools, that model provider wants them to work with (for example specific tools under claude code), and hence they perform better.
I think this will always be the case, unless someone tunes open models to work with the tools that their coding agent will use.
Some open models have specific training for defined tools (a notable example is OpenAI GPT-OSS and its "built in" tools for browser use and python execution (they are called built in tools, but they are really tool interfaces it is trained to use if made available.) And closed models are also trained to work with generic tools as well as their “built in” tools.
Among these, I had lots of trouble getting GLM-4.7-Flash to work (failed tool calls etc), and even when it works, it's at very low tok/s. On the other hand Qwen3 variants perform very well, speed wise. For local sensitive document work, these are excellent; for serious coding not so much.
One caviat missed in most instructions is that you have to set CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC = 1 in your ~/.claude/settings.json, otherwise CC's telemetry pings cause total network failure because local ports are exhausted.
[1] claude-code-tools local LLM setup: https://github.com/pchalasani/claude-code-tools/blob/main/do...
I get that it's not going to work as well as hosted/subscription services like Claude/Gemini/Codex/..., but sometimes those aren't an option
Essentially: `ollama launch claude`
we can do much better with a cheap model on openrouter (glm 4.7, kimi, etc.) than anything that I can run on my lowly 3090 :)
Thanks again for this info & setup guide! I'm excited to play with some local models.
Local models are relatively small, it seems wasteful to try and keep them as generalists. Fine tuning on your specific coding should make for better use of their limited parameter count.
For example choosing a model that knows rails 8 and react development running on a mac and using docker
Ideally that would make the model small enough to be competitive running locally
I wrote this for the scenario you've run out of quota for the day or week but want a back up plan to keep going to give some options with obvious speed and quality trade-offs. There is also always the option to upgrade if your project and use case needs Opus 4.5.
Going dumb/cheap just ends up costing more, in the short and long term.
Has anyone had a better experience?
I've been running CC with Qwen3-Coder-30B (FP8) and I find it just as fast, but not nearly as clever.
Will it work? Yes. Will it produce same quality as Sonnet or Opus? No.
https://docs.z.ai/devpack/tool/claude
https://www.cerebras.ai/blog/introducing-cerebras-code
or i guess one of the hosted gpu providers
if you're basically a homelabber and wanted an excuse to run quantized models on your own device go for it but dont lie and mutter under your own tin foil hat that its a realistic replacement
(Also, I love your podcast!)
But I was really disappointed when I tried to use subagents. In theory I really liked the idea: have Haiku wrangle small specific tasks that are tedious but routine and have Sonnet orchestrate everything. In practice the subagents took so many steps and wrote so much documentation that it became not worth it. Running 2-3 agents blew through the 5 hour quota in 20 minutes of work vs normal work where I might run out of quota 30-45 minutes before it resets. Even after tuning the subagent files to prevent them from writing tests I never asked for and not writing tons of documentation that I didn’t need they still produced way too much content and blew the context window of the main agent repeatedly. If it was a local model I wouldn’t mind experimenting with it more.
This is with my regular $20/month ChatGpT subscription and my $200 a year (company reimbursed) Claude subscription.
Right now OpenAI is giving away fairly generous free credits to get people to try the macOS Codex client. And... it's quite good! Especially for free.
I've cancelled my Anthropic subscription...
Google significantly reduced the free quota and removed pro models from gemini cli some 2-3 moths ago.
Also, Gemini models eat tokens like crazy. Something Codex and Code would do with 2K tokens takes Gemini 100K. Not sure why.
When I hit the usage limit on the first account, I simply switch to the second and continue working. Since Claude stores progress locally rather than tying it to a specific account, the session picks up right where it left off. That makes it surprisingly seamless to keep momentum without waiting for limits to reset.
And they do? That's what the API is.
The subscription always seemed clearly advertised for client usage, not general API usage, to me. I don't know why people are surprised after hacking the auth out of the client. (note in clients they can control prompting patterns for caching etc, it can be cheaper)
The API is for using the model directly with your own tools. It can be in dev, or experiments, or anything.
Subscriptions are for using the apps Claude + code. That's what it always said when you sign up.
LLMs are a hyper-competitive market at the moment, and we have a wealth of options, so if Anthropic is overpricing their API they'll likely be hurting themselves.
Wildly understating this part.
Even the best local models (ones you run on beefy 128GB+ RAM machines) get nowhere close to the sheer intelligence of Claude/Gemini/Codex. At worst these models will move you backwards and just increase the amount of work Claude has to do when your limits reset.
I was using GLM on ZAI coding plan (jerry rigged Claude Code for $3/month), but finding myself asking Sonnet to rewrite 90% of the code GLM was giving me. At some point I was like "what the hell am I doing" and just switched.
To clarify, the code I was getting before mostly worked, it was just a lot less pleasant to look at and work with. Might be a matter of taste, but I found it had a big impact on my morale and productivity.
This is a very common sequence of events.
The frontier hosted models are so much better than everything else that it's not worth messing around with anything lesser if doing this professionally. The $20/month plans go a long way if context is managed carefully. For a professional developer or consultant, the $200/month plan is peanuts relative to compensation.
Unless you include it in "frontier", but that has usually been used to refer to "Big 3".
(At todays ram prices upgrading to that for me would pay for a _lot_ of tokens...)
4x rtx 6000 pro is probably the minimum you need to have something reasonable for coding work.
I'd probably rather save the capex, and use the rented service until something much more compelling comes along.
Kimi K2.5 is good, but it's still behind the main models like Claude's offerings and GPT-5.2. Yes, I know what the benchmarks say, but the benchmarks for open weight models have been overpromising for a long time and Kimi K2.5 is no exception.
Kimi K2.5 is also not something you can easily run locally without investing $5-10K or more. There are hosted options you can pay for, but like the parent commenter observed: By the time you're pinching pennies on LLM costs, what are you even achieving? I could see how it could make sense for students or people who aren't doing this professionally, but anyone doing this professionally really should skip straight to the best models available.
Unless you're billing hourly and looking for excuses to generate more work I guess?
I still have to occasionally switch to Opus in Opencode planning mode, but not having to rely on Sonnet anymore makes my Claude subscription last much longer.
I'm not looking for a vibe coding "one-shot" full project model. I'm not looking to replace GPT 5.2 or Opus 4.5. But having a local instance running some Ralph loop overnight on a specific aspect for the price of electricity is alluring.
I've also been testing OpenClaw. It burned 8M tokens during my half hour of testing, which would have been like $50 with Opus on the API. (Which is why everyone was using it with the sub, until Anthropic apparently banned that.)
I was using GLM on Cerebras instead, so it was only $10 per half hour ;) Tried to get their Coding plan ("unlimited" for $50/mo) but sold out...
(My fallback is I got a whole year of GLM from ZAI for $20 for the year, it's just a bit too slow for interactive use.)
Because of how the plugin works in VS code, on my third day of testing with Claude Code, I didn't click the Claude button and was accidentally working with CoPilot for about three hours of torture when I realized I wasn't in Claude Code. Will NEVER make that mistake again... I can only imagine anything I can run at any decent speed lcoally will be closer to the latter. I pretty quickly reach a "I can do this faster/better myself" point... even a few times with Claude/Opus, so my patience isn't always the greatest.
That said, I love how easy it is to build up a scaffold of a boilerplate app for the sole reason to test a single library/function in isolation from a larger application. In 5-10 minutes, I've got enough test harness around what I'm trying to work on/solve that it lets me focus on the problem at hand, while not worrying about doing this on the integrated larger project.
I've still got some thinking and experimenting to do with improving some of my workflows... but I will say that AI Assist has definitely been a multiplier in terms of my own productivity. At this point, there's literally no excuse not to have actual code running experiments when learning something new, connecting to something you haven't used before... etc. in terms of working on a solution to a problem. Assuming you have at least a rudimentary understanding of what you're actually trying to accomplish in the piece you are working on. I still don't have enough trust to use AI to build a larger system, or for that matter to truly just vibe code anything.
Kimi K2.5 is a trillion parameter model. You can't run it locally on anything other than extremely well equipped hardware. Even heavily quantized you'd still need 512GB of unified memory, and the quantization would impact the performance.
Also the proprietary models a year ago were not that good for anything beyond basic tasks.
Are there a lot of options how "how far" do you quantize? How much VRAM does it take to get the 92-95% you are speaking of?
So many: https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overvie...
> How much VRAM does it take to get the 92-95% you are speaking of?
For inference, it's heavily dependent on the size of the weights (plus context). Quantizing an f32 or f16 model to q4/mxfp4 won't necessarily use 92-95% less VRAM, but it's pretty close for smaller contexts.
Number of params == “variables” in memory
VRAM footprint ~= number of params * size of a param
A 4B model at 8 bits will result in 4GB vram give or take, same as params. At 4 bits ~= 2GB and so on. Kimi is about 512GB at 4 bits.
Now as the other replies say, you should very likely run a quantized version anyway.
https://buildai.substack.com/i/181542049/the-mac-mini-moment
> Thermal throttling: Thunderbolt 5 cables get hot under sustained 15GB/s load. After 10 minutes, bandwidth drops to 12GB/s. After 20 minutes, 10GB/s. Your 5.36 tokens/sec becomes 4.1 tokens/sec. Active cooling on cables helps but you’re fighting physics.
Thermal throttling of network cables is a new thing to me…
I'm completely over these hypotheticals and 'testing grade'.
I know Nvidia VRAM works, not some marketing about 'integrated ram'. Heck look at /r/locallama/ There is a reason its entirely Nvidia.
That's simply not true. NVidia may be relatively popular, but people use all sorts of hardware there. Just a random couple of recent self-reported hardware from comments:
- https://www.reddit.com/r/LocalLLaMA/comments/1qw15gl/comment...
- https://www.reddit.com/r/LocalLLaMA/comments/1qw0ogw/analysi...
- https://www.reddit.com/r/LocalLLaMA/comments/1qvwi21/need_he...
- https://www.reddit.com/r/LocalLLaMA/comments/1qvvf8y/demysti...
Then you sent over links describing such.
In real world use, Nvidia is probably over 90%.
You have a point that at scale everybody except maybe Google is using Nvidia. But r/locallama is not your evidence of that, unless you apply your priors, filter out all the hardware that don't fit your so called "hypotheticals and 'testing grade'" criteria, and engage in circular logic.
PS: In fact locallamma does not even cover your "real world use". Most mentions of Nvidia are people who have older GPUs eg. 3090s lying around, or are looking at the Chinese VRAM mods to allow them run larger models. Nobody is discussing how to run a cluster of H200s there.
>moderate context windows
Really had to modify the problem to make it seem equal? Not that quants are that bad, but the context windows thing is the difference between useful and not useful.
This is a _remarkably_ aggressive comment!
0. https://www.daifi.ai/
I'll add on https://unsloth.ai/docs/models/qwen3-coder-next
The full model is supposedly comparable to Sonnet 4.5 But, you can run the 4 bit quant on consumer hardware as long as your RAM + VRAM has room to hold 46GB. 8 bit needs 85.
No one's running Sonnet/Gemini/GPT-5 locally though.
https://www.youtube.com/watch?v=bFgTxr5yst0
I've never heard of this guy before, but I see he's got 5M YouTube subscribers, which I guess is the clout you need to have Apple loan (I assume) you $50K worth of Mac Studios!
I'll be interesting to see how model sizes, capability, and local compute prices evolve.
A bit off topic, but I was in best buy the other day and was shocked to see 65" TVs selling for $300 ... I can remember the first large flat screen TVs (plasma?) selling for 100x that ($30K) when they first came out.
Great demo video though. Nice to see some benchmarks of Exo with this cluster across various models.
Instead have Claude know when to offload work to local models and what model is best suited for the job. It will shape the prompt for the model. Then have Claude review the results. Massive reduction in costs.
btw, at least on Macbooks you can run good models with just M1 32GB of memory.
Although I'm starting to like LMStudio more, as it has more features that Ollama is missing.
https://lmstudio.ai
You can then get Claude to create the MCP server to talk to either. Then a CLAUDE.md that tells it to read the models you have downloaded, determine their use and when to offload. Claude will make all that for you as well.
The big powerful models think about tasks, then offload some stuff to a drastically cheaper cloud model or the model running on your hardware.
But as a counterpoint: there are whole communities of people in this space who get significant value from models they run locally. I am one of them.
If you’re worried about others being able to clone your business processes if you share them with a frontier provider then the cost of a Mac Studio to run Kimi is probably a justifiable tax right off.
Before that I used Qwen3-30B which is good enough for some quick javascript or Python, like 'add a new endpoint /api/foobar which does foobaz'. Also very decent for a quick summary of code.
It is 530Tok/s PP and 50Tok/s TG. If you have it spit out lots of the code that is just copy of the input, then it does 200Tok/s, i.e. 'add a new endpoint /api/foobar which does foobaz and return the whole file'
It is probably enough to handle a lot of what people use the big-3 closed models for. Somewhat slower and somewhat dumber, granted, but still extraordinarily capable. It punches way above its weight class for an 80B model.
Hell, if you are willing to go even slower, any GPU + ~80GB of RAM will do it.
I need to do more testing before I can agree that it is performing at a Sonnet-equivalent level (it was never claimed to be Opus-class.) But it is pretty cool to get beaten in a programming contest by my own video card. For those who get it, no explanation is necessary; for those who don't, no explanation is possible.
And unlike the hosted models, the ones you run locally will still work just as well several years from now. No ads, no spying, no additional censorship, no additional usage limits or restrictions. You'll get no such guarantee from Google, OpenAI and the other major players.
Which definitely has some questionable implications... but just like with advertising it's not like paying makes the incentives for the people capable of training models to put their thumbs on the scales go away.
I expect it'll come along but I'm not gonna spend the $$$$ necessary to try to DIY it just yet.
PC or Mac? A PC, ya, no way, not without beefy GPUs with lots of VRAM. A mac? Depends on the CPU, an M3 Ultra with 128GB of unified RAM is going to get closer, at least. You can have decent experiences with a Max CPU + 64GB of unified RAM (well, that's my setup at least).
For VS Code code completion in Continue using a Qwen3-coder 7b model. For CLI work Qwen coder 32b for sidebar. 8 bit quant for both.
I need to take a look at Qwen3-coder-next, it is supposed to have made things much faster with a larger model.
That said, Claude Code is designed to work with Anthropic's models. Agents have a buttload of custom work going on in the background to massage specific models to do things well.
That might incentivize it to perform slightly better from the get go.
I have claude pro $20/mo and sometimes run out. I just set ANTHROPIC_BASE_URL to a localllm API endpoint that connects to a cheaper Openai model. I can continue with smaller tasks with no problem. This has been done for a long time.
Whether it's a giant corporate model or something you run locally, there is no intelligence there. It's still just a lying engine. It will tell you the string of tokens most likely to come after your prompt based on training data that was stolen and used against the wishes of its original creators.