AtomicChat/gemma-4-31B-it-GGUF overview

---

license: apache-2.0

license_link: https://ai.google.dev/gemma/docs/gemma_4_license

thumbnail: https://huggingface.co/AtomicChat/gemma-4-31B-it-GGUF/resolve/main/hero.png

base_model:

• google/gemma-4-31B-it

base_model_relation: quantized

quantized_by: AtomicChat

pipeline_tag: image-text-to-text

library_name: gguf

tags:

• atomic-chat
• gemma
• gemma4
• google
• gguf
• llama.cpp
• imatrix
• quantized

---

<center>

<div style="display:flex; justify-content:center; align-items:center; gap:2%; max-width:560px; margin:0 auto;">

<a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/gemma-4-31B-it-GGUF/resolve/main/pill_atomic_v3.png" alt="Atomic Chat" style="width:100%; height:auto; max-width:186px;"></a>

<a href="https://discord.gg/8wGSsvmg4V" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/gemma-4-31B-it-GGUF/resolve/main/pill_discord_v3.png" alt="Join Discord" style="width:100%; height:auto; max-width:184px;"></a>

<a href="https://github.com/AtomicBot-ai/Atomic-Chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/gemma-4-31B-it-GGUF/resolve/main/pill_github_v3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a>

</div>

<br/>

<img src="https://huggingface.co/AtomicChat/gemma-4-31B-it-GGUF/resolve/main/hero.png" alt="Gemma 4 31B" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/>

<div style="display:flex; justify-content:center; gap:0.5em;">

<a href="https://huggingface.co/google/gemma-4-31B-it"><strong>Base model: google/gemma-4-31B-it</strong></a>

</div>

</center>

Gemma 4 31B, self-quantized to GGUF by Atomic Chat. Built straight from Google's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.

Highlights

• 30.7B parameters: the weights this repo quantizes.
• Context length: 256K tokens, as published by Google.
• 60 layers: Dense decoder, hybrid sliding-window (1024) and global attention.
• Modalities: Text, Image.
• Full imatrix ladder: every quant is calibrated with an importance matrix, published here alongside the quants.
• Reasoning: All models in the family are designed as highly capable reasoners, with configurable thinking modes.
• Diverse & Efficient Architectures: Offers Dense and Mixture-of-Experts (MoE) variants of different sizes for scalable deployment.

> [!NOTE]

> These GGUFs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

> [!IMPORTANT]

> Always pass --jinja so the Gemma 4 31B chat template is applied. Without it the model can emit malformed turns.

Model Overview

| Property | Value |

|---|---|

| Base model | google/gemma-4-31B-it |

| Parameters | 30.7B |

| Layers | 60 |

| Sliding window | 1024 tokens |

| Context length | 256K tokens |

| Vocabulary | 262K |

| Modalities | Text, Image |

| Architecture | Dense decoder, hybrid sliding-window (1024) and global attention, 32 attention heads over 16 KV heads, Gemma4ForConditionalGeneration |

| This repo | GGUF quants (imatrix) and a vision mmproj; the importance matrix is published here as imatrix-coding.gguf. Quants: Q2_K, IQ3_M, Q3_K_M, Q3_K_L, IQ4_XS, Q4_K_S, Q4_K_M, UD-Q4_K_XL, Q5_K_S, Q5_K_M, Q6_K, Q8_0 |

> [!NOTE]

> Gemma 4 31B is multimodal. This repo ships the mmproj-gemma4-31b-it-f16.gguf vision projector. With -hf it is pulled automatically; otherwise pass --mmproj. Use llama-mtmd-cli or llama-server to feed images.

<img src="https://huggingface.co/AtomicChat/gemma-4-31B-it-GGUF/resolve/main/benchmark.png" alt="Gemma 4 31B benchmark scores" style="width:100%; max-width:900px;"/>

Scores are Google's published results for the base google/gemma-4-31B-it, not our own measurements. Quantization preserves the large majority of this; Q4_K_M and up stay close to full precision.

Choosing a quant

| Quant | Size | Notes |

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

| Q2_K | 11.9 GB | Smallest K-quant. Minimal RAM, clear quality drop. |

| IQ3_M | 14.2 GB | Beats Q3 at a similar size thanks to imatrix. Best low-RAM pick. |

| Q3_K_M | 15.3 GB | Low quality but usable. |

| Q3_K_L | 16.6 GB | A step above Q3_K_M. |

| IQ4_XS | 16.7 GB | Excellent quality for size. Recommended low-bit. |

| Q4_K_S | 17.8 GB | Compact 4-bit, fast. |

| Q4_K_M | 18.7 GB | Recommended default. Best balance of size, speed and quality. |

| UD-Q4_K_XL | 19.0 GB | Dynamic. Embeddings and output kept at Q8_0 for higher quality at a Q4 footprint. |

| Q5_K_S | 21.3 GB | Higher quality, slightly more compact than Q5_K_M. |

| Q5_K_M | 21.8 GB | Higher quality, low loss. |

| Q6_K | 25.2 GB | Near lossless, noticeably lighter than Q8_0. |

| Q8_0 | 32.6 GB | Effectively lossless, reference quality. |

> [!TIP]

> Pick the largest file that fits your (V)RAM with room for context. Q4_K_M or UD-Q4_K_XL is the sweet spot for most setups; Q6_K or Q8_0 for maximum fidelity.

Get started

Run Gemma 4 31B locally with:

• Atomic Chat: the easiest path. Open the app, search AtomicChat/gemma-4-31B-it-GGUF, pick a quant, hit Use this model.
• llama.cpp: llama-server -hf AtomicChat/gemma-4-31B-it-GGUF:Q4_K_M --jinja -c 8192
• Ollama: ollama run hf.co/AtomicChat/gemma-4-31B-it-GGUF:Q4_K_M
• LM Studio / Jan: search the repo id, download any quant.

Best practices

| Parameter | Value |

|---|---|

| temperature | 1.0 |

| top_p | 0.95 |

| top_k | 64 |

Google's recommended sampling configuration for google/gemma-4-31B-it. Pass images through llama-mtmd-cli or llama-server with the projector.

Run in llama.cpp

git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --target llama-cli llama-server

./llama.cpp/build/bin/llama-server \
    -hf AtomicChat/gemma-4-31B-it-GGUF:Q4_K_M \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

1. Download google/gemma-4-31B-it (original weights).
2. Convert to f16 GGUF with llama.cpp.
3. Build an importance matrix over our calibration corpus, published here as imatrix-coding.gguf.
4. Quantize the ladder with --imatrix.
5. UD-Q4_K_XL additionally pins the token-embedding and output tensors to Q8_0.

License

Original model by Google, released under the Apache 2.0 license. Full terms: Apache 2.0. Quantized by Atomic Chat.