Qwen3.5-9B has been extremely useful for local fuzzy table extraction OCR for data that cannot be sent to the cloud.

The documents have subtly different formatting and layout due to source variance. Previously we used a large set of hierarchical heuristics to catch as many edge cases as we could anticipate.

Now with the multi-modal capabilities of these models we can leverage the language capabilities along side vision to extract structured data from a table that has 'roughly this shape' and 'this location'.

I used vLLM and qwen3-coder-next to batch-process a couple million documents recently. No token quota, no rate limits, just 100% GPU utilization until the job was done.

Idk about everyone else, but I don’t want to rent tokens forever. I want a self hosted model that is completely private and can’t be monitored or adulterated without me knowing. I use both currently, but I am excited at the prospect of maybe not having to in the near to mid future.

I’ve increasingly started self hosting everything in my home lately because I got tired of SAAS rug pulls and I don’t see why LLM’s should eventually be any different.

I've been largely using Qwen3.5-122b at 6 bit quant locally for some c++/go/python dev lately because it is quite capable as long as I can give it pretty specific asks within the codebase and it will produce code that needs minimal massaging to fit into the project.

I do have a $20 claude sub I can fall back to for anything qwen struggles with, but with 3.5 I have been very pleased with the results.

Some tasks don’t require SOTA models. For translating small texts I use Gemma 4 on my iPhone because it’s faster and better than Apple Translate or Google Translate and works offline. Also if you can break down certain tasks like JSON healing into small focused coding tasks then local models are useful

I use LMStudio to host and run GLM 4.7 Flash as a coding agent. I use it with the Pi coding agent, but also use it with the Zed editor agent integrations. I've used the Qwen models in the past, but have consistently come back to GLM 4.7 because of its capabilities. I often use Qwen or Gemma models for their vision capabilities. For example, I often will finish ML training runs, take a photo of the graphs and visualizations of the run metrics and ask the model to tell me things I might look at tweaking to improve subsequent training runs. Qwen 3.5 0.8b is pretty awesome for really small and quick vision tasks like "Give me a JSON representation of the cards on this page".

It’s easy to find a combination of llama.cpp and a coding tool like OpenCode for these. Asking an LLM for help setting it up can work well if you don’t want to find a guide yourself.

> and finding more value than just renting tokens from Anthropic of OpenAI?

Buying hardware to run these models is not cost effective. I do it for fun for small tasks but I have no illusions that I’m getting anything superior to hosted models. They can be useful for small tasks like codebase exploration or writing simple single use tools when you don’t want to consume more of your 5-hour token budget though.

The people i know that use local models just end up with both.

The local models don’t really compete with the flagship labs for most tasks

But there are things you may not want to send to them for privacy reasons or tasks where you don’t want to use tokens from your plan with whichever lab. Things like openclaw use a ton of tokens and most of the time the local models are totally fine for it (assuming you find it useful which is a whole different discussion)

While they can be run locally, and most of the discussion on HN about that, I bet that if you look at total tok/day local usage is a tiny amount compared to total cloud inference even for these models. Most people who do use them locally just do a prompt every now and then.

The privacy/data security angle really is important in some regions and industries. Think European privacy laws or customers demanding NDAs. The value of Anthropic and OpenAI is zero for both cases, so easy to beat, despite local models being dumber and slower.

They are okay for vibe coding throw-away projects without spending your Anthrophic/OAI tokens

I was thinking the same thing. My only guess is that they are excited about local models because they can run it cheaper through Open Router ?

I am working on a research project to link churches from their IRS Exempt org BMF entry to their google search result from 10 fetched. Gwen2.5-14b on a 16gb Mac Mini. It works good enough!

It's entertaining to see HN increasingly consider coding harness as the only value a model can provide.

There are really nice GUIs for LLMs - CherryStudio for example, can be used with local or cloud models.

There are also web-UIs - just like the labs ones.

And you can connect coding agents like Codex, Copilot or Pi to local coding agents - the support OpenAI compatible APIs.

It's literally a terminal command to start serving the model locally and you can connect various things to it, like Codex.