Gemma 4 31B
, self-quantized to GGUF by
Atomic Chat
. Built straight from Google's original weights with a per-tensor importance matrix. Runs fully offline.
Highlights
Multimodal
— natively handles text and image input and generates text output.
Built-in reasoning
— designed as a capable reasoner with a configurable thinking mode set via the system prompt.
256K context window
for long documents and codebases.
Multilingual
— out-of-the-box support for 35+ languages, pre-trained on 140+ languages.
Coding & agentic
— native function-calling support and notable coding-benchmark improvements.
Native system prompt
— native support for the
system
role for more structured conversations.
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.
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
Total parameters
30.7B (Dense)
Layers
60
Context length
256K tokens
Vocabulary
262K
Architecture
Dense decoder, hybrid local/global attention with Proportional RoPE
This repo
GGUF quants (imatrix) + vision mmproj
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.
Scores are Google's published results for the base
google/gemma-4-31B-it
. Quantization preserves the large majority of this;
Q4_K_M
and up sit within a point or two of full precision.
Choosing a quant
Quant
Size
Notes
Q2_K
11.9 GB
Smallest. Minimal RAM, clear quality drop.
IQ3_M
14.2 GB
Beats Q3 at 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 Q4, 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
19.6 GB
Higher quality.
Q5_K_M
17.4 GB
Higher quality, low loss.
Q6_K
7.6 GB
Near lossless.
Q8_0
18.4 GB
Effectively lossless, reference quality.
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/gemma4-31b-it-GGUF
, pick a quant, hit
Use this model
.
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