I had good experience with Codex iterating to prove a fixed point theorem. But will also now consider GLM-4.6.

they have a Claude Code specific endpoint...see the excellent docs https://docs.z.ai/devpack/tool/claude

I use liteLLM local running on Docker.

https://www.litellm.ai/

I tried synthetic.new prior to GLM-4.6...Starting in August...So I already had a subscription.

When z.ia launched GLM-4.6, I subscribed to their Coding Pro plan. Although I haven't been coding as heavy this month as the prior two months, I used to hit Claude limits almost daily, often twice a day. That was with both the $20 and $100 plans. I have yet to hit a limit with z.ai and the server response is at least as good as Claude.

I mention synthetic.new as it's good to have options and I do appreciate them sponsoring the dev of Octofriend. z.ai is a China company and I think hosts in Singapore. That could be a blocker for some.

Z.ai is on the US Entities (banned from export/collab) list:

> “These entities advance the People’s Republic of China’s military modernization through the development and integration of advanced artificial intelligence research. This activity is contrary to the national security and foreign policy interests of the United States under Section 744.11 of the EAR.”

https://medium.com/ai-disruption/zhipu-ai-chinas-leading-lar...

https://z.ai/subscribe

$6/month. It's $3 for the first month (or first months, on longer subscription cycles, but it's first unit of subscription cycle at half price only).

At $6/month it's still pretty reasonable, IMO, and chucking less than $10 at it for three months probably gets you to the next pop-up token retailer offering introductory pricing, so long as the bubble doesn't burst before then.

And that'll be two orders of magnitude slower right?

Not necessarily. You need either multiple GPUs or unified memory. There are a handful of UM platforms out there nowadays (mainly Macs but AMD has some as well albeit none with 300GB ram)

I'm looking forward to future ollama releases that might attempt parity with the cloud offerings. I've since moved onto the Ollama compatibility API on KoboldCPP since they don't have any such limits with their inference server.

How does Qwen3-Coder:30B compare to Instruct-2507 as a coding agent backend? I was under the impression that Instruct was intended to supersede Coder?

In this case, it's not about whether it fits on my physical hardware or not. It's about what seems like an arbitrary restriction designed to start pushing users to their cloud offering.

Tokens are expensive, sure, but I don't even _want_ Ollama to run inference for me.

Ollama gives me, essentially, a wrapper for llama.cpp and convenient hosting where I can download models.

I'm happy to pay for the bandwidth, plus a premium to cover their running this service.

I'm furthermore happy to pay a small charge to cover the development that they've done and continue to do to make local-inference easy for me.

> I don't see any other way that Ollama can not be enshittified without making a quick buck.

Me neither. The mistake they did was getting outside investments, as now they're no longer in full control and eventually are gonna have to at least give the impression they give a shit about the investors, and it'll come at the cost of the users one way or another.

Please pay for your tools that are independently developed, we really need more community funding of projects so we can avoid this never-ending spiral of VC-fueled+killed tools.

> I don’t know how much Ollama contributes to llama.cpp

If nothing else, Ollama is free publicity for llama.cpp, at least when they acknowledge they're mostly using the work of llama.cpp, which has happened at least once! I found llama.cpp by first finding Ollama and then figured I'd rather avoid the lock-in of Ollama's registry, so ended up using llama.cpp for everything.

The answer is 0

I can not answer for GG, but the early days of llama.cpp were crazy and everything was so very hacky. Remember, Textgen-webui was 'the way' to run models at first because it supported so many different quant types and file extensions. At the time, most people were using multiple different quantization methods and it was really hard to figure out which were performing better or worse objectively.

GGUF/GGML was like the 4th iteration of file type quantization from llama.cpp and I remember that I had to consciously begin watching the bandwidth usage from my ISP. Up to that point, I had never received an email warning me about reaching limits of my 2TB connection. All for the same models just in different forms. TheBloke was pumping out models like he had unlimited time/effort.

I say all that to say, llama.cpp was still trying, dare I say 'inventing', all the things throughout these transitions. Ollama comes in to make the running part easier and less CLI flag dependent building off of llama.cpp. Awesome.

GG and company are down in the trenches of the models architecture with CUDA, Vulkan, CPU, ROCm, etc. They are working on perplexity, token processing/generation and just look at the 'bin' folder when you compile the project. There are so many different aspects to make the whole thing work as well at it does. It's amazing that we have llama-server at all with the amount of work that has gone into making llama.cpp.

All that to say, Ollama shit the bed on attribution. They were called out on r/localllama very early on for not really giving credit to llama.cpp. They have a soiled reputation with the people that participate in that sub-reddit at least. They were called out for not contributing back if I remember correctly as well, which further stained their reputation among the folks who hang in that sub-reddit.

So it's not a matter of "ease" to build what Ollama built... At least from the perspective of someone who has been paying close attention from r/localllama; the problem was/is simply the perception (right or wrong) of the meme; Person 2 to person 1: "You built this?" -> Person 2: takes item/thing -> person 2: Holds up item/thing -> "I built this". A simple act that really pissed off the community in general.

>what is gg working on

supporting models so ollama can then 'support' them too

if you use llama.cpp server, it's quite a nice experience. you can even directly download stuff from Huggingface.

I recently tested every version from 0.7 to 0.11.1 trying to run q5 mistral-3.1 on a system with 48GB of available vram across 2 GPUs. Everything past 0.7.0 gave me OOM or other errors. Now that I've migrated back to llama.cpp I'm not particularly interested in fucking around with ollama again.

as for 4chan, they've hated ollama for a long time because they built on top of llama.cpp and then didn't contribute upstream or give credit to the original project

man i don't know, rick. i'm just reading comments on hacker news but maybe the one llama.cpp called out by GP could be a place to look? not sure, rick.