πŸ“‹ Model Description


quantized_by: bartowski pipeline_tag: image-text-to-text base_model: google/gemma-3-4b-it

Llamacpp imatrix Quantizations of gemma-3-4b-it by google

Using llama.cpp release b4877 for quantization.

Original model: https://huggingface.co/google/gemma-3-4b-it

All quants made using imatrix option with dataset from here

Run them in LM Studio

Run them directly with llama.cpp, or any other llama.cpp based project

Vision

This model has vision capabilities, more details here: https://github.com/ggml-org/llama.cpp/pull/12344

After building with Gemma 3 clip support, run the following command:

./build/bin/llama-gemma3-cli -m googlegemma-3-4b-it-Q80.gguf --mmproj mmproj-google_gemma-3-4b-it-f16.gguf

Prompt format

<bos><startofturn>user
{system_prompt}

{prompt}<endofturn>
<startofturn>

Download a file (not the whole branch) from below:

FilenameQuant typeFile SizeSplitDescription
mmproj-gemma-3-4b-it-f32.gguff321.68GBfalseF32 format MMPROJ file, required for vision.
mmproj-gemma-3-4b-it-f16.gguff16851MBfalseF16 format MMPROJ file, required for vision.
gemma-3-4b-it-bf16.ggufbf167.77GBfalseFull BF16 weights.
gemma-3-4b-it-Q80.ggufQ804.13GBfalseExtremely high quality, generally unneeded but max available quant.
gemma-3-4b-it-Q6KL.ggufQ6KL3.35GBfalseUses Q80 for embed and output weights. Very high quality, near perfect, recommended.
gemma-3-4b-it-Q6K.ggufQ6K3.19GBfalseVery high quality, near perfect, recommended.
gemma-3-4b-it-Q5KL.ggufQ5KL2.99GBfalseUses Q80 for embed and output weights. High quality, recommended.
gemma-3-4b-it-Q5KM.ggufQ5K_M2.83GBfalseHigh quality, recommended.
gemma-3-4b-it-Q5KS.ggufQ5K_S2.76GBfalseHigh quality, recommended.
gemma-3-4b-it-Q4KL.ggufQ4KL2.65GBfalseUses Q80 for embed and output weights. Good quality, recommended.
gemma-3-4b-it-Q41.ggufQ412.56GBfalseLegacy format, similar performance to Q4KS but with improved tokens/watt on Apple silicon.
gemma-3-4b-it-Q4KM.ggufQ4K_M2.49GBfalseGood quality, default size for most use cases, recommended.
gemma-3-4b-it-Q3KXL.ggufQ3KXL2.40GBfalseUses Q80 for embed and output weights. Lower quality but usable, good for low RAM availability.
gemma-3-4b-it-Q4KS.ggufQ4K_S2.38GBfalseSlightly lower quality with more space savings, recommended.
gemma-3-4b-it-Q40.ggufQ402.37GBfalseLegacy format, offers online repacking for ARM and AVX CPU inference.
gemma-3-4b-it-IQ4NL.ggufIQ4NL2.36GBfalseSimilar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference.
gemma-3-4b-it-IQ4XS.ggufIQ4XS2.26GBfalseDecent quality, smaller than Q4KS with similar performance, recommended.
gemma-3-4b-it-Q3KL.ggufQ3K_L2.24GBfalseLower quality but usable, good for low RAM availability.
gemma-3-4b-it-Q3KM.ggufQ3K_M2.10GBfalseLow quality.
gemma-3-4b-it-IQ3M.ggufIQ3M1.99GBfalseMedium-low quality, new method with decent performance comparable to Q3KM.
gemma-3-4b-it-Q3KS.ggufQ3K_S1.94GBfalseLow quality, not recommended.
gemma-3-4b-it-Q2KL.ggufQ2KL1.89GBfalseUses Q80 for embed and output weights. Very low quality but surprisingly usable.
gemma-3-4b-it-IQ3XS.ggufIQ3XS1.86GBfalseLower quality, new method with decent performance, slightly better than Q3KS.
gemma-3-4b-it-Q2K.ggufQ2K1.73GBfalseVery low quality but surprisingly usable.
gemma-3-4b-it-IQ3XXS.ggufIQ3XXS1.69GBfalseLower quality, new method with decent performance, comparable to Q3 quants.
gemma-3-4b-it-IQ2M.ggufIQ2M1.54GBfalseRelatively low quality, uses SOTA techniques to be surprisingly usable.

Embed/output weights

Some of these quants (Q3KXL, Q4KL etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

Downloading using huggingface-cli


Click to view download instructions

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download bartowski/googlegemma-3-4b-it-GGUF --include "googlegemma-3-4b-it-Q4KM.gguf" --local-dir ./

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download bartowski/googlegemma-3-4b-it-GGUF --include "googlegemma-3-4b-it-Q8_0/*" --local-dir ./

You can either specify a new local-dir (googlegemma-3-4b-it-Q80) or download them all in place (./)

ARM/AVX information

Previously, you would download Q4044/48/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.

Now, however, there is something called "online repacking" for weights. details in this PR. If you use Q40 and your hardware would benefit from repacking weights, it will do it automatically on the fly.

As of llama.cpp build b4282 you will not be able to run the Q40XX files and will instead need to use Q4_0.

Additionally, if you want to get slightly better quality for , you can use IQ4NL thanks to this PR which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.


Click to view Q40X_X information (deprecated

I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.


Click to view benchmarks on an AVX2 system (EPYC7702)

modelsizeparamsbackendthreadstestt/s% (vs Q4_0)
qwen2 3B Q4_01.70 GiB3.09 BCPU64pp512204.03 Β± 1.03100%
qwen2 3B Q4_01.70 GiB3.09 BCPU64pp1024282.92 Β± 0.19100%
qwen2 3B Q4_01.70 GiB3.09 BCPU64pp2048259.49 Β± 0.44100%
qwen2 3B Q4_01.70 GiB3.09 BCPU64tg12839.12 Β± 0.27100%
qwen2 3B Q4_01.70 GiB3.09 BCPU64tg25639.31 Β± 0.69100%
qwen2 3B Q4_01.70 GiB3.09 BCPU64tg51240.52 Β± 0.03100%
qwen2 3B Q4KM1.79 GiB3.09 BCPU64pp512301.02 Β± 1.74147%
qwen2 3B Q4KM1.79 GiB3.09 BCPU64pp1024287.23 Β± 0.20101%
qwen2 3B Q4KM1.79 GiB3.09 BCPU64pp2048262.77 Β± 1.81101%
qwen2 3B Q4KM1.79 GiB3.09 BCPU64tg12818.80 Β± 0.9948%
qwen2 3B Q4KM1.79 GiB3.09 BCPU64tg25624.46 Β± 3.0483%
qwen2 3B Q4KM1.79 GiB3.09 BCPU64tg51236.32 Β± 3.5990%
qwen2 3B Q408_81.69 GiB3.09 BCPU64pp512271.71 Β± 3.53133%
qwen2 3B Q408_81.69 GiB3.09 BCPU64pp1024279.86 Β± 45.63100%
qwen2 3B Q408_81.69 GiB3.09 BCPU64pp2048320.77 Β± 5.00124%
qwen2 3B Q408_81.69 GiB3.09 BCPU64tg12843.51 Β± 0.05111%
qwen2 3B Q408_81.69 GiB3.09 BCPU64tg25643.35 Β± 0.09110%
qwen2 3B Q408_81.69 GiB3.09 BCPU64tg51242.60 Β± 0.31105%
Q408_8 offers a nice bump to prompt processing and a small bump to text generation

Which file should I choose?


Click here for details

A great write up with charts showing various performances is provided by Artefact2 here

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

If you don't want to think too much, grab one of the K-quants. These are in format 'QXKX', like Q5KM.

If you want to get more into the weeds, you can check out this extremely useful feature chart:

llama.cpp feature matrix

But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQXX, like IQ3M. These are newer and offer better performance for their size.

These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

The I-quants are not compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.

Credits

Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.

Thank you ZeroWw for the inspiration to experiment with embed/output.

Thank you to LM Studio for sponsoring my work.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

πŸ“‚ GGUF File List

πŸ“ Filename πŸ“¦ Size ⚑ Download
google_gemma-3-4b-it-IQ2_M.gguf
LFS Q2
1.43 GB Download
google_gemma-3-4b-it-IQ3_M.gguf
LFS Q3
1.85 GB Download
google_gemma-3-4b-it-IQ3_XS.gguf
LFS Q3
1.74 GB Download
google_gemma-3-4b-it-IQ3_XXS.gguf
LFS Q3
1.57 GB Download
google_gemma-3-4b-it-IQ4_NL.gguf
LFS Q4
2.2 GB Download
google_gemma-3-4b-it-IQ4_XS.gguf
LFS Q4
2.11 GB Download
google_gemma-3-4b-it-Q2_K.gguf
LFS Q2
1.61 GB Download
google_gemma-3-4b-it-Q2_K_L.gguf
LFS Q2
1.76 GB Download
google_gemma-3-4b-it-Q3_K_L.gguf
LFS Q3
2.08 GB Download
google_gemma-3-4b-it-Q3_K_M.gguf
LFS Q3
1.95 GB Download
google_gemma-3-4b-it-Q3_K_S.gguf
LFS Q3
1.8 GB Download
google_gemma-3-4b-it-Q3_K_XL.gguf
LFS Q3
2.23 GB Download
google_gemma-3-4b-it-Q4_0.gguf
Recommended LFS Q4
2.21 GB Download
google_gemma-3-4b-it-Q4_1.gguf
LFS Q4
2.39 GB Download
google_gemma-3-4b-it-Q4_K_L.gguf
LFS Q4
2.47 GB Download
google_gemma-3-4b-it-Q4_K_M.gguf
LFS Q4
2.32 GB Download
google_gemma-3-4b-it-Q4_K_S.gguf
LFS Q4
2.21 GB Download
google_gemma-3-4b-it-Q5_K_L.gguf
LFS Q5
2.79 GB Download
google_gemma-3-4b-it-Q5_K_M.gguf
LFS Q5
2.64 GB Download
google_gemma-3-4b-it-Q5_K_S.gguf
LFS Q5
2.57 GB Download
google_gemma-3-4b-it-Q6_K.gguf
LFS Q6
2.97 GB Download
google_gemma-3-4b-it-Q6_K_L.gguf
LFS Q6
3.12 GB Download
google_gemma-3-4b-it-Q8_0.gguf
LFS Q8
3.85 GB Download
google_gemma-3-4b-it-bf16.gguf
LFS FP16
7.23 GB Download
mmproj-google_gemma-3-4b-it-bf16.gguf
LFS FP16
811.82 MB Download
mmproj-google_gemma-3-4b-it-f16.gguf
LFS FP16
811.82 MB Download
mmproj-google_gemma-3-4b-it-f32.gguf
LFS
1.56 GB Download