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

Name: gemma-4-31B-it-assistant-GGUF
Availability: InStock

google/gemma-4-31B-it-assistant

quantized

apache-2.0

endpoints_compatible

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Base model: google/gemma-4-31B-it-assistant

Gemma 4 31B It Assistant, 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: the base model handles Text, Image; this repo ships text-only quants, it carries no vision projector.
Full imatrix ladder: every quant is calibrated with an importance matrix.
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 It Assistant chat template is applied. Without it the model can emit malformed turns.

Model Overview

Property	Value
Base model	`google/gemma-4-31B-it-assistant`
Parameters	30.7B
Layers	60
Sliding window	1024 tokens
Context length	256K tokens
Vocabulary	262K
Modalities	Text, Image in the base model; text only in this repo, it ships no vision projector
Architecture	Dense decoder, hybrid sliding-window (1024) and global attention, 32 attention heads over 16 KV heads, `Gemma4AssistantForCausalLM`
This repo	GGUF quants (imatrix). Quants: `Q4_K_S`, `Q4_K_M`, `Q5_K_M`, `Q8_0`, `F16`

Benchmarks

Benchmark	Score
MMLU Pro	85.2%
AIME 2026 no tools	89.2%
LiveCodeBench v6	80.0%
Codeforces ELO	2150
GPQA Diamond	84.3%
Tau2 (average over 3)	76.9%
HLE no tools	19.5%
HLE with search	26.5%
BigBench Extra Hard	74.4%
MMMLU	88.4%
MMMU Pro	76.9%
OmniDocBench 1.5 (average edit distance, lower is better)	0.131
MATH-Vision	85.6%
MedXPertQA MM	61.3%
MRCR v2 8 needle 128k (average)	66.4%

Scores are Google's published results for the base google/gemma-4-31B-it-assistant, 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
`Q4_K_S`	349 MB	Compact 4-bit, fast.
`Q4_K_M`	353 MB	Recommended default. Best balance of size, speed and quality.
`Q5_K_M`	377 MB	Higher quality, low loss.
`Q8_0`	0.5 GB	Effectively lossless, reference quality.
`F16`	1.0 GB	Unquantized reference, twice the size of Q8_0.

[!TIP] Pick the largest file that fits your (V)RAM with room for context. Q4_K_M is the sweet spot for most setups; Q6_K or Q8_0 for maximum fidelity.

Get started

Run Gemma 4 31B It Assistant locally with:

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

Run in llama.cpp

bash

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

bash

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

How these were made

Download google/gemma-4-31B-it-assistant (original weights).
Convert to f16 GGUF with llama.cpp.
Build an importance matrix over our calibration corpus.
Quantize the ladder with --imatrix.

License

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