What was the original core principle of ollama?

I had used oobabooga back in the day and found ollama unnecessary.

Adding Caddy as a proxy server is literally one line in Caddyfile, and I trust Caddy to do it right once more than I trust every other random project to add SSL.

Because adding a caddy/nginx/apache + letsencrypt is a couple of bash commands between install and setup, and those http servers + TLS termination is going to be 100x better than what LMS adds themselves, as it isn't their core competency.

Tailscale serve

thats why i use caddy or ngrok.ai

For context, LMStudio has had a CLI for a while it just required the desktop app to be open already. This makes it where you can run LMStudio properly headless and not just from a terminal while the desktop app is open.

`lms chat` has existed, `lms daemon up` / "llmster" is the new command.

Yes, frontier models from the labs are a step ahead and likely will always be, but we've already crossed levels of "good enough for X" with local models. This is analogous to the fact that my iPhone 17 is technically superior to my iPhone 8, but my outcomes for text messaging are no better.

I've invested heavily in local inference. For me, it's a mixture privacy, control, stability, cognitive security.

Privacy - my agents can work on tax docs, personal letters, etc.

Control - I do inference steering with some projects: constraining which token can be generated next at any point in time. Not possible with API endpoints.

Stability - I had many bad experiences with frontier labs' inference quality shifting within the same day, likely due to quantization due to system load. Worse, they retire models, update their own system prompts, etc. They're not stable.

Cognitive Security - This has become more important as I rely more on my agents for performing administrative work. This is intermixed with the Control/Stability concerns, but the focus is on whether I can trust it to do what I intended it to do, and that it's acting on my instructions, rather than the labs'.

> What’s the main use-case for this?

Running weights available models.

> I get that I can run local models, but all the paid for (remote) models are superior.

If that's clearly true for your use cases, then maybe this isn’t for you.

> So is the use-case just for people who don’t want to use big tech’s models?

Most weights available models are also “big tech’s”, or finetunes of them.

> Is this just for privacy conscious people? Or is this just for “adult” chats, ie porn bots?

Sure, those are among the use cases. And there can be very good reasons to be concerned about privacy in some applications. But they aren’t the only reasons.

There’s a diversity of weights-available models available, with a variety of specialized strengths. Sure, for general use, the big commercial models may generally be more capable, but they may not be optimal for all uses (especially when cost effectiveness is considered, given that capable weights-available models for some uses are very lightweight.)

For many tasks you don't really need big models. And relatively small model, quantized too can be run on your macbook (not to mention Mac studio).

Not always. Besides, this allows one to use a post-trained model, a heretic model, an abliterated model, or their own.

I exclusively run local models. On par with Opus 4.5 for most things. gpt-oss is pretty capable. Qwen3 as well.

I originally used local models as a somewhat therapeutic/advice thing. I didn't want to give openAI all my dirt.

But then I decided I'm just a chemical reaction and a product of my environment, so I gave chatGPT all my dirt anyway.

But before, I cared about my privacy.

I've gotten interested in local models recently after trying the here and there for years. We've finally hit the point where small <24GB models are capable of pretty amazing things. One use I have is I have a scraped forum database, and with a 20gb devstral model I was able to get it to select a bunch of random posts related to a species of exotic plants in batches of 5-10 up to n, summarize them into and intern sqllite table, then at the end go through read the interim summarization and write a final document addressing 5 different topics related to users experience growing the species.

Thats what convinced me they are ready to do real work, are they going to replace claude code...not currently. But it is insane to me that such a small model can follow those explicit directions and consistently perform that workflow.

I've during that experimentation, even when not putting the sql explicit it was able to craft the queries on its own from just text description, and has no issue navigating the cli and file system doing basic day to day things.

I'm sure there are a lot of people doing "adult" things, but my interest is sparked because they finally at the level they can be a tool in a homelab, and no longer is llm usage limits subsidized like they used to be. Not to mention I am really disillusioned with big tech having my data or exposing a tool making API calls to them that then can make actions on my system.

I'll still keep using claude code day to day coding. But for small system based tasks I plan on moving to local llms. Their capabilities have inspired me to write my own agentic framework to see what work flows can be put together for just management and automation of day to day task. Ideally it would be nice to just chat with an llm and tell it to add an appointment or call at x time or make sure I do it that day and it can read my schedule and remind-me at a chill time of my day to make the call, and then check up that I followed through. I also plan on seeing if I can also set it up to remind me and help to practice mindfulness and just general stress management I should do. While sure a simple reminder might work, but as someone with adhd who easily forgets reminders as soon as they pop up if I can get to them now, being pestered by an agent that wakes up and engages with me seems like it might be an interesting workflow.

And the hacker aspect, now that they are capable I really want to mess around with persistent knowledge in databases and making them intercommunicate and work together. Might even give them access to rewrite themselves and access the application during run time with a lisp. But to me local llms have gotten to the point they are fun and not annoying. I can run a model that is better than chatgpt 3.5 for the most part, its knowledge is more distilled and narrower, but for what they do understand their correctness is much better.

TL;DR: The classic CIA triad: Confidentiality, Integrity, Availability; cost/price concerns; the leading open-weight models aren't nearly as bad as you might think.

You don't need LM Studio to run local models, it just (was, formerly), a nice UI to download and manage HF models and llama.cpp updates, quickly and easily manually switch between CPU / Vulkan / ROCm / CUDA (depending on your platform).

Regarding your actual question, there are several reasons.

First off, your allusion to privacy - absolutely, yes, some people use it for adult role-play, however, consider the more productive motivations for privacy, too: a lot of businesses with trade secrets they may want to discuss or work on with local models without ever releasing that information to cloud providers, no matter how much those cloud providers pinky promise to never peek at it. Google, Microsoft, Meta, et al have consistently demonstrated that they do not value or respect customer privacy expectations, that they will eagerly comply with illegal, unconstitutional NSA conspiracies to facilitate bulk collection of customer information / data. There is no reason to believe Anthropic, OpenAI, Google, xAI would act any differently today. In fact, there is already a standing court order forcing OpenAI to preserve all customer communications, in a format that can be delivered to the court (i.e. plaintext, or encryption at rest + willing to provide decryption keys to the court), in perpetuity (https://techstartups.com/2025/06/06/court-orders-openai-to-p...)

There are also businesses which have strict, absolute needs for 24/7 availability and low latency, which remote APIs never have offered. Even if the remote APIs were flawless, and even if the businesses have a robust multi-WAN setup with redundant UPS systems, network downtime or even routing issues are more or less an inevitable fact of life, sooner or later. Having local models means you have inference capability as long as you have electricity.

Consider, too, the integrity front: frontier labs may silently modify API-served models to be lower quality for heavy users with little means of detection by end users (multiple labs have been suspected / accused of this; a lack of proof isn't evidence that it didn't happen) or that the API-served models can be modified over time to patch behaviors that may have been previously relied upon for legitimate workloads (imagine a red team that used a jailbreak to get a model to produce code for process hollowing, for instance). This second example absolutely has happened with almost every inference provider.

The open weight local models also have zero marginal cost besides electricity once the hardware is present, unlike PAYG API models, which create financial lock-in and dependency that is in direct contrast with the financial interests of the customers. You can argue about the amortized costs of hardware, but that's a decision for the customer to make using their specific and personal financial and capex / hardware information that you don't have at the end of the day.

Further, the gap between frontier open weight models and frontier proprietary models has been rapidly shrinking and continues to. See Kimi K2.5, Xiaomi MiMo v2, GLM 4.7, etc. Yes, Opus 4.5, Gemini 3 Pro, GPT-5.2-xhigh are remarkably good models and may beat these at the margin, but most work done via LLMs does not need the absolute best model; many people will opt for a model that gets 95% of the output quality of the absolute frontier model when it can be had for 1/20th the cost (or less).

To justify investing a trillion dollars like everything else LLM-related. The local models are pretty good. Like I ran a test on R1 (the smallest version) vs Perplexity Pro and shockingly got better answers running on base spec Mac Mini M4. It's simply not true that there is a huge difference. Mostly it's hardcoded overoptimalization. In general these models aren't really becoming better.

are parallel requests "free"? or do you half performance when sending two requests in parallel?

Awesome - having the API, MCP integrations, refined CLI give you everything you might want. I have some things I'd wanted to try with ChainForge and LMStudio that are now almost trivial.

Thanks for the updates!

This is a breeze to do with llama.cpp, which has had Anthropic responses API support for over a month now.

On your inference machine:

  you@yourbox:~/Downloads/llama.cpp/bin$ ./llama-server -m <path/to/your/model.gguf> --alias <your-alias> --jinja --ctx-size 32768 --host 0.0.0.0 --port 8080 -fa on

Then, on the system you're running Claude Code on:

  export ANTHROPIC_BASE_URL=http://<ip-of-your-inference-system>:<port>
  export ANTHROPIC_AUTH_TOKEN="whatever"
  export CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1
  claude --model <your-alias> [optionally: --system "your system prompt here"]

What’s the best open source alternative?

Right now it looks like as VS Code (give or take). Pretty sure both are\will be used by many professionals.

"looks like a toy" has very little to do with its use anyway.

woah dude, take it easy. There are no missing features, there are more feature. You might just not be finding them where they were before. Remember this is still 0.x, why would the devs be stuck and not be able to improve the UI just because of past decisions?