---
license: apache-2.0
tags:
  - gguf
  - llama.cpp
  - onboard
  - understand
---

# OnBoard recommended models (GGUF)

The models OnBoard's installer offers, mirrored here for
stable hosting. All files are 4-bit GGUF quantizations for
llama.cpp-based serving (shipped with OnBoard/Understand as `ullama`), all
under the Apache-2.0 license, mirrored unmodified from the source repos below.

Every model here has been through our own qualification testing on real
Understand project data: a chat test that measures how well the model answers
questions about a codebase using OnBoard's analysis tools, and a code-summary
benchmark that grades generated overviews for accuracy against the source.
The notes below come from those measurements, not from vendor claims. That
said, AI is AI — every model gets things wrong, so treat any answer as a
starting point and take it with a grain of salt.

## Which model should I use?

Run `ullama-recommend` (shipped with OnBoard and Understand) — it looks at
your machine's memory and suggests the right size. In short: pick the largest
one your machine runs comfortably, and prefer the gemma models when you want
the best chat answers.

| Model | Download | Why you would pick it |
|---|---|---|
| gpt-oss-120b | 63 GB (2 parts) | The most accurate code summaries in our testing, and quick for its size. Needs roughly a 128 GB machine. |
| Qwen3.6-35B-A3B | 22.4 GB | Highly accurate code summaries, and much faster than other models this large. |
| Gemma 4 12B | 7.1 GB | The best chat answers of any model we tested. |
| Qwen3.5 9B | 5.7 GB | Did well in chat testing, and digs a little deeper into code before answering. |
| Gemma 4 E4B | 5.0 GB | Did well in our chat testing. |
| Gemma 4 E2B | 3.1 GB | OnBoard's default model. Good at both chat and code summaries. |
| Qwen3.5 2B | 1.3 GB | Writes good code summaries remarkably fast, but struggled in our chat testing. |



## Files, licensing, and provenance

| File | Model | Company (Country) | License | Source |
|---|---|---|---|---|
| gpt-oss-120b-Q4_K_M-0000?-of-00002.gguf | gpt-oss-120b | OpenAI (United States) | Apache-2.0 | [unsloth/gpt-oss-120b-GGUF](https://huggingface.co/unsloth/gpt-oss-120b-GGUF) |
| Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf | Qwen3.6-35B-A3B | Alibaba (China) | Apache-2.0 | [unsloth/Qwen3.6-35B-A3B-GGUF](https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF) |
| gemma-4-12b-it-Q4_K_M.gguf | Gemma 4 12B | Google (United States) | Apache-2.0 | [unsloth/gemma-4-12b-it-GGUF](https://huggingface.co/unsloth/gemma-4-12b-it-GGUF) |
| Qwen3.5-9B-Q4_K_M.gguf | Qwen3.5 9B | Alibaba (China) | Apache-2.0 | [unsloth/Qwen3.5-9B-GGUF](https://huggingface.co/unsloth/Qwen3.5-9B-GGUF) |
| gemma-4-E4B-it-Q4_K_M.gguf | Gemma 4 E4B | Google (United States) | Apache-2.0 | [unsloth/gemma-4-E4B-it-GGUF](https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF) |
| gemma-4-E2B-it-Q4_K_M.gguf | Gemma 4 E2B | Google (United States) | Apache-2.0 | [unsloth/gemma-4-E2B-it-GGUF](https://huggingface.co/unsloth/gemma-4-E2B-it-GGUF) |
| Qwen3.5-2B-Q4_K_M.gguf | Qwen3.5 2B | Alibaba (China) | Apache-2.0 | [unsloth/Qwen3.5-2B-GGUF](https://huggingface.co/unsloth/Qwen3.5-2B-GGUF) |

Quantization policy: Q4_K_M for models ≤12B; Unsloth Dynamic (UD-Q4_K_XL) for
the large MoE, where the dynamic quant's quality edge matters most. gpt-oss-120b
is a two-part split: download both parts into the same directory and point the
server at part 1 — llama.cpp finds the second part automatically.

All models retain their original Apache-2.0 licenses; no modifications were
made beyond mirroring the original GGUF files.