mchiang

hey, thanks for sharing. I had to go to the Twitter feed to find the GitHub link:

https://github.com/21st-dev/1code

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.

I am super hopeful! Hardware is improving, inference costs will continue to decrease, models will only improve...

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.

ah! This must be downloaded from elsewhere and not from Ollama? So sorry about this.

To help future optimizations for given quantizations, we have been trying to limit the quantizations to ones that fit for majority of users.

In the case of mistral-small3.1, Ollama supports ~4bit (q4_k_m), ~8bit (q8_0) and fp16.

https://ollama.com/library/mistral-small3.1/tags

I'm hopeful that in the future, more and more model providers will help optimize for given model quantizations - 4 bit (i.e. NVFP4, MXFP4), 8 bit, and a 'full' model.

Do you find yourself sticking with GLM 4.6 over Claude for some tasks? Or do you find yourself still wanting to reach for Claude?

https://github.com/ollama/ollama?tab=readme-ov-file#supporte...

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?

but that is VC funded

sorry, I don't use 4chan, so I don't know what's said there.

May I ask what system you are using where you are getting memory estimations wrong? This is an area Ollama has been working on and improved quite a bit on.

Latest version of Ollama is 0.12.5 and with a pre-release of 0.12.6

0.7.1 is 28 versions behind.

It’s so much fun when you can mix passion and seeing students chat about what they want to build the most.

Oh yes! that is why I want to provide the names of the providers we use. I do believe in building in the open. The web search functionality has a very generous free tier (it is behind Ollama's free account to prevent abuse) that allows you to give it a try comparing to running a searxng server locally.

On making the search functionality locally -- we made considerations and gave it a try but had trouble around result quality and websites blocking Ollama for making a crawler. Using a hosted API, we can get results for users much faster. I'd want us to revisit this at some point. I believe in having the power of local.

No, Ollama is it's own project and separate. You can check it out via GitHub

https://github.com/ollama/ollama

Sorry about this. We are working really hard on providing a usage based pricing.

During the preview period we want to start offering a $20 / month plan tailored for individuals - and we are monitoring the usage and making changes as people hit rate limits so we can satisfy most use cases, and be generous.

We have relationships with many providers and I don't want to be seen as promoting or not promoting a specific provider. Some decent privacy-preserving vendors - Brave, Exa, Parallel Web Systems, DuckDuckGo etc

We will continue to monitor what's good to improve the output quality and results. Sometimes it could be the combination of providers to yield even better results. If I say one combination right now, and realize another combination is better, and make changes, I wouldn't need to broadcast it each time or risk misrepresenting the feature, which is to have amazing search and research capabilities that can augment models for a superior output.

To provide additional features or using Ollama's cloud hosted models, you can signup for an Ollama account.

For starter, this is completely optional. It can be completely local too for you to publish your own models to ollama.com that you can share with others.

We work with search providers and ensure that we have zero data retention policies in place.

The search results are yours to own and use. You are free to do what you want with it. Of course you are bound by local laws of the legal jurisdiction you are in.

When we started Ollama, we were told how open-source (open-weight wasn't a term back then) will always be inferior to the close-sourced models. This was 2 years ago (Ollama's birthday is July 18th, 2023).

Fast forward to now, open models are quickly catching up, and at a significantly lower price point for most and can be customized for specific tasks instead of being general purpose. For general purpose models, absolutely the closed models are currently dominating.

Fair question. Some of the supported models are large and wouldn't fit on most local devices. This is just the beginning, and Ollama does not need to exclude cloud hosted frontier models either with the relationship we've built with the model providers. We just have to be mindful and understand that Ollama stands with developers, and solve the needs.

https://ollama.com/cloud

I haven't tried SearXNG personally. How does it compare to Ollama's web search in terms of the search content returned?

I was pleasantly surprised on the model improvements when testing this feature.

For smaller models, it can augment it with the latest data by fetching it from the web, solving the problem of smaller models lacking specific knowledge.

For larger models, it can start functioning as deep research.

It’s a different repo. https://github.com/ggml-org/ggml

The models are implemented by Ollama https://github.com/ollama/ollama/tree/main/model/models

I can say as a fact, for the gpt-oss model, we also implemented our own MXFP4 kernel. Benchmarked against the reference implementations to make sure Ollama is on par. We implemented harmony and tested it. This should significantly impact tool calling capability.

Im not sure if im feeding here. We really love what we do, and I hope it shows in our product, in Ollama’s design and in our voice to our community.

You don’t have to like Ollama. That’s subjective to your taste. As a maintainer, I certainly hope to have you as a user one day. If we don’t meet your needs and you want to use an alternative project, that’s totally cool too. It’s the power of having a choice.

I can say trying many inference tools after the launch, many do not have the models implemented well, and especially OpenAI’s harmony.

Why does this matter? For this specific release, we benchmarked against OpenAI’s reference implementation to make sure Ollama is on par. We also spent a significant amount of time getting harmony implemented the way intended.

I know vLLM also worked hard to implement against the reference and have shared their benchmarks publicly.

so sorry about this. We are learning. Possible to email, and we will first make it right while we improve Ollama's turbo mode. hello@ollama.com

thanks, I'll take that feedback, but I do want to clarify that it's not from llama.cpp/ggml. It's from ggml-org/ggml. I supposed it's all interchangeable though, so thank you for it.

hmm, I don't think so. This is more of, we want to keep improving Ollama so we can have a great core.

For the users who want GPUs, which cost us money, we will charge money for it. Completely optional.

it's all open, and specifically, the new models are implemented here: https://github.com/ollama/ollama/tree/main/model/models

totally respect your choice, and it's a great project too. Of course as a maintainer of Ollama, my preference is to win you over with Ollama. If it doesn't meet your needs, it's okay. We are more energized than ever to keep improving Ollama. Hopefully one day we will win you back.

Ollama does not use llama.cpp anymore; we do still keep it and occasionally update it to remain compatible for older models for when we used it. The team is great, we just have features we want to build, and want to implement the models directly in Ollama. (We do use GGML and ask partners to help it. This is a project that also powers llama.cpp and is maintained by that same team)

OpenAI has only provided MXFP4 weights. These are the same weights used by other cloud providers.

hmm, how so? Ollama is open and the pricing is completely optional for users who want additional GPUs.

Is it bad to fairly charge money for selling GPUs that cost us money too, and use that money to grow the core open-source project?

At one point, it just has to be reasonable. I'd like to believe by having a conscientious, we can create something great.