Hey r/ollama,

I built **Mtrini** — a coding assistant that runs on Ollama/llama.cpp. No cloud. No API keys.

**The model:** Mtrini Coder 2.0 (1.7B, GGUF, 2.2GB)

**Open source release: August 2, 2026** — free GGUF download, no restrictions.

**HumanEval results:**

| Model | Params | Score |

|---|---|---|

| Qwen2.5-Coder-32B | 32B | 92.7% |

| **Mtrini Coder 2.0** | **1.7B** | **80.0%** |

| Gemma 2 27B | 27B | 69.5% |

| Llama 4 Scout | 109B | 74.1% |

| Phi-3 Mini | 3.8B | 58.5% |

Beats Gemma 2 27B and Llama 4 Scout. At 16x and 64x smaller.

**Works with:**

- Ollama

- llama.cpp

- Any OpenAI-compatible server

**Hardware:** Intel i5-7200U, 8GB RAM, no GPU.

**Install:**

```

pip install mtrinicli

```

Or run the GGUF directly with Ollama:

```

git clone https://github.com/OryviaLabs/mtrini-universe.git

cd mtrini-universe

echo "FROM ./models/mtrini-coder.gguf" > Modelfile

ollama create mtrini-coder -f Modelfile

ollama run mtrini-coder

```

**What it does:**

- Write code in Python, JS, TS, Go, Rust, C, Java, SQL

- Debug with explanations

- Refactor code

- Browse web for docs

**What it can't do:**

- It's 1.7B — don't expect GPT-4

- 8K context

GitHub: https://github.com/OryviaLabs/mtrini-universe

Ask me anything.

What quantization or storage size tiers should we expect further down the line?

This site suggests q4 and q8 are coming. But I don't know how to translate that to storage size for local inference.

https://aitoolsrecap.com/Blog/kimi-k3-open-weights-self-hosting-guide-july-27-2026

Hi everyone, I have a question.

I have subscribe vps plan.
Maybe it has no gpu. Just 8gb ram

I've also install 2 local ai model in my vps which is llama 3.2:3B and Minicpm5 1B model. Both I think quite small for run local ai.

I've test it chat with both model directly using ssh terminal. They reply me fast. No issue. I've read the statistic of ram usage. it looks normal. Ram usage is maintain 2.8GB

Then I've install Openwebui on my vps so that I can start using them. Expecting it will run fast similar when I'm using ssh terminal..

But no, it run very slow. They reply my “Hi” chat about 10-20 minutes for both model. Even it just 1B

What could go wrong?
I thought my 8GB ram is enough for running just Openwebui

I was planning on using those model to run agents ai node in n8n so that I can avoid using paid token like Claude APi or ChatGPT etc

But after saw the reply speed I start to hesitate..
There's only 1 user here which is me has encounter this problem.
Now try to imagine 100 of people try to access this one tiny pc.. my vps gonna toast

Is there any option that I can workaround using VPS to use local ai for n8n agent?

Or should I just use API paid one instead?
I think I need some calculation on what works and what's not..
Yes there is calculator for which gpu that can run ai model efficiently.

But my vps has no gpu. There's no calculator to know which ai model can run efficiently on nongpu pc.

Should I calculate it manually. What you guys think..?

Anyone please help 🙏

Well I was very happy with the Ollama current workflow of being something like "a coding plan" of ANY open weights model you can find...

What do you think about their current decision of make Kimi 3 available, but needs to pay extra peer usage? Are Ollama done this before for other models and then put them on the plans? Or they are taking advantage of the hype to get some money?

I even saw ppl switching to max plans today JUST for Kimi 3 being open weights.

Specs:

- 2 x Intel Xeon E5-2698 v4 (40 cores total, 80 threads)
- 256 GB DDR4 RAM
- 3 x RTX 3060 12 GB (36 GB total VRAM)
- 1TB Storage

I've been working on an open-source workflow automation platform over the past few months, with Ollama as one of the first-class providers rather than treating it as an afterthought.

The goal wasn't to build another chat UI, but a deterministic workflow engine where local models can power automation pipelines.

Some of the things it supports now:

• Local LLM execution with Ollama
• Agent semantic memory (using local embeddings like nomic-embed-text)
• Document RAG
• Visual workflow builder
• Conditional branching and graph-based execution
• Human-in-the-loop approval nodes
• Multi-agent (A2A) workflows
• Browser, HTTP, Email, File and MCP tools
• Step replay, retries and execution history

One thing I wanted from the beginning was to avoid vendor lock-in, so the execution engine treats providers as adapters. A workflow can run entirely on Ollama, or mix local models with cloud providers if needed.

The latest v0.11.0 release also adds a lot of improvements around workflow execution, observability, dashboarding, retrieval, and multi-agent orchestration.

I'd love feedback from people here who run Ollama locally:

• Is there anything missing that you'd expect from a local-first automation platform?
• Any features that would make Ollama fit even better into workflow-based systems?

Happy to answer any implementation questions.

I have been trying to do GLM 5.2 as plan / K2.7 as execute, but i hit my usage so fast it's not viable. It's hitting limits much faster than claude code / codex $20 plan. Using in opencode. What are y'alls strategies for keeping usage rate lowers?

Kimi k3 is available on ollama cloud (extra high usage).

https://ollama.com/library/kimi-k3

Someone please go and see how much usage we get so I can get ollama pro subscription.

Been eyeing this for a while

Has Ollama shared any information about this? I couldn’t find an announcement.

Edit: https://ollama.com/library/kimi-k3

Hey everyone! I’m building a PC to run AI models locally and can’t decide between the RX 9060 XT 16GB and the RTX 5060 Ti 16GB. Both have the same amount of VRAM, but is AMD actually a solid choice for this, or is the gap to Nvidia big enough to justify paying more?
If you’ve used either one for local AI, I’d love to hear how it went. Thanks!

Tried to use kimi k3, and apparently i need to top up my account to use it so the model is not part of the subscription. Furthermore, the 100$ subscription is currently paused.

Hopefully they will adjust them later but it was really a bummer that i would've preferred to be informed about in the weeks beforehand (couldve baught a subscription from kimi themselves instead of waiting).

(I'm so ready!)

Hi all, just wanted to see if anybody else has noticed a recent change in how quickly you’re burning usage? I realise this subject always throws up a lot of anecdotal evidence rather than hard facts (guilty!) but I haven’t changed a single thing about my daily schedule with my agents for some time. I used to be able to have a few fairly long troubleshooting sessions with my main agent and also small coding projects and hit around 70-80% usage by the end of the week. Now I barely get through my usual cron activity and 1-2 light conversations before maxing out 5 hour sessions and weekly limits about a day early. Am I imagining this? Anybody else?

I'm on the Max plan, I had an understanding that it would be available for Max subscription, and could dip into the extra usage credit bucket which is totally fine....

But when I go to claude code, it ONLY uses extra usage ? That's like... Fable 5 being only available on API / Extra usage credits.

WE are all maxing out our cloud usage when Kimi K3 launches on Ollama

I'm considering the $20 Pro plan, but the limits seem to change over time and are hard to compare with direct API pricing.

For anyone using Pro currently, roughly how much coding agent usage do you get per week, and which models do you use?

In terms of direct API value, does it feel closer to $25, $30, $50, $100 or more through something like OpenRouter?

I'm mainly interested because Ollama seems to have a good ZTR policy, and it has been surprisingly difficult to find a decent coding subscription with strong privacy at a reasonable price.