i mean they have attributed but also it's open source software, i guess the more meaningful question is why didn't ggerganov build Ollama if it was that easy? or what is his company working on now?

Ollama is more than a paste. But the support for GLM 4.6 is indeed coming from llama.cpp: https://github.com/ollama/ollama/issues/12505#issuecomment-3...

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

Z.ai team is awesome and very supportive. I have yet to try synthetic.new. What's the reason for using multiple? Is it mainly to try different models or are you hitting some kind of rate limit / usage limit?

My experience using GLM-4.6 with Charm Crush has been absolutely incredible, especially with high thinking. This is on pretty hard tasks too, e.g. proving small lemmas with Lean.

I've had much less luck with other agentic software, including Claude Code. For these kinds of tasks, only Codex seems to come close.

No, you need multiple GPUs. These models are not intended to be run by the average user.

You can buy an M3 Ultra Mac Studio and configure it with 512 GB of memory shared between the CPU and the GPU. Will set you back about $9500.

Qwen3-coder:30b is in the blog post. This is one that most users will be able to run locally.

We are in this together! Hoping for more models to come from the labs in varying sizes that will fit on devices.

I know this is disappointing, but what business model would be best here for ollama?

1. Donationware - Let's be real, tokens are expensive and if they ask for everyone to chip in voluntarily people wouldn't do that and Ollama would go bust quickly.

2. Subscriptions (bootstrapped and no VCs) again like 1. people would have to pay for the cloud service as a subscription to be sustainable (would you?) or go bust.

3. Ads - Ollama could put ads in the free version but to remove them the users can pay for a higher tier, a somewhat good compromise, except developers don't like ads and don't like pay for their tools unless their company does it for them. No users = Ollama goes bust.

4. VCs - This is the current model which is why they have a cloud product and it keeps the main product free (for now). Again, if they cannot make money or sell to another company Ollama goes bust.

5. Fully Open Source (and 100% free) with Linux Foundation funding - Ollama could also go this route, but this means they wouldn't be a business anymore for investors and rely on the Linux Foundation's sponsors (Google, IBM, etc) for funding the LF to stay sustainable. The cloud product may stay for enterprises.

Ollama has already taken money from investors so they need to produce a return for them so 5. isn't an option in the long term.

6. Acquisition by another company - Ollama could get acquired and the product wouldn't change* (until the acquirer jacks up prices or messes with the product) which ultimately kills it anyway as the community moves on.

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

You just need to avoid VC backed tools and pay for bootstrapped ones without any ties to investors.

Aren't these models consistently quite large and hard to run locally? It's possible that future Ollama releases will allow you to dynamically manage VRAM memory in a way that enables these models to run with acceleration on even modest GPU hardware (such as by dynamically loading layers for a single 'expert' into VRAM, and opportunistically batching computations that happen to rely on the same 'expert' parameters - essentially doing manually what mmap does for you in CPU-only inference) but these 'tricks' will nonetheless come at non-trivial cost in performance.

this one is exciting. It'll enable and accelerate a lot of devices on Ollama - especially around AMD GPUs not fully supported by ROCm, Intel GPUs, and iGPUs across different hardware vendors.