Gemma-3-27B Open-source Conversational Large Model - Free Deployment for High-quality Text Generation

Gemma 3 27b It Qat GGUF

Developed by ubergarm

Gemma-3-27B is a quantized-optimized conversational large language model supporting advanced non-linear quantization techniques, delivering high-quality text generation capabilities.

Large Language Model Open Source License:MIT #Non-linear quantization optimization #Low VRAM inference #Large context processing

Downloads 852

Release Time : 4/19/2025

Model Overview

This model is a quantized version based on Google Gemma 3 27B parameter model, designed for efficient inference, supporting conversational interactions, and suitable for various text generation tasks.

Model Features

Advanced non-linear quantization

Uses the ik_llama.cpp branch to support SotA non-linear quantization technology, providing the best perplexity performance at the same memory footprint.

Efficient memory management

Supports multiple quantization configurations and KV cache optimization, significantly reducing VRAM usage and adapting to different hardware environments.

Long context support

Supports up to 32k context length, suitable for processing long documents and complex dialogue scenarios.

Model Capabilities

Conversational interaction

Long text generation

Multi-turn dialogue processing

Use Cases

Dialogue systems

Intelligent customer service

Used to build multi-turn customer service systems capable of handling complex queries

Maintains dialogue coherence at 32k context length

Content creation

Long article generation

Generates coherent long-form technical documents or creative writing

Perplexity 8.1755 (iq4_ks quantized version)

🚀 `ik_llama.cpp` imatrix Quantizations of google/gemma-3-27b-it-qat-q4_0-unquantized

This project focuses on the imatrix quantizations of google/gemma-3-27b-it-qat-q4_0-unquantized using ik_llama.cpp. It provides high - quality quantized models with excellent perplexity for a given memory footprint.

✨ Features

This quant collection REQUIRES the ik_llama.cpp fork to support advanced non - linear SotA quants. Do not download these large files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc!
These quants offer the best in - class perplexity for the given memory footprint.

📄 License

The project is licensed under the MIT license.

Property	Details
Quantized By	ubergarm
Pipeline Tag	text - generation
Base Model	google/gemma-3-27b-it-qat-q4_0-unquantized
License	mit
Base Model Relation	quantized
Tags	imatrix, gemma - 3, conversational, ik_llama.cpp

📦 Installation

This quant collection REQUIRES the ik_llama.cpp fork to support advanced non - linear SotA quants. Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc!

💻 Usage Examples

Basic Usage

`ik_llama.cpp` API server for GPU inferencing

# This example for 24GB VRAM
./build/bin/llama-server \
    --alias ubergarm/gemma-3-27b-it-qat-mix-iq3_k.gguf \
    --model /mnt/raid/models/ubergarm/gemma-3-27b-it-qat-GGUF/gemma-3-27b-it-qat-iq4_ks.gguf \
    -ctk q8_0 -ctv q8_0 \
    -fa \
    -amb 512 \
    -fmoe \
    -c 32768 \
    -ub 512 \
    -ngl 99 \
    --threads 4 \
    --host 127.0.0.1 \
    --port 8080

Advanced Usage

If you want more context and/or less VRAM usage, you can try:

Smaller KV Cache quantization -ctk q4_0 -ctv q4_0
Runtime Repack for CPU inferencing, override attn tensors to CPU, disable KV offload -rtr -ot attn=CPU -nkvo.

📚 Documentation

Quant Collection

So far these are the best recipes offering the lowest perplexity per GiB models. Check out this speed and quality comparison benchmarks graphs and discussion.

ubergarm/gemma-3-27B-it-qat-iq4_ks.gguf

Best Quality

32k context in 23704MiB VRAM
16k context in 19488MiB VRAM
8k context in 17380MiB VRAM
Only 13126MiB VRAM with -rtr -ot attn=CPU -nkvo
Could go q4_0 kv cache for lower VRAM usage!

14.099 GiB (4.484 BPW)
f32:  373 tensors
type q4_0:   62 tensors blk.*.attn_v.weight
type q8_0:    1 tensors
iq4_ks:  372 tensors
Final estimate: PPL = 8.1755 +/- 0.06296

ubergarm/gemma-3-27B-it-qat-mix-iq3_k.gguf

Smallest with Good Quality

32k context in 22306MiB VRAM
16k context in 18090MiB VRAM
8k context in 15982MiB VRAM
Only 11960MiB VRAM with -rtr -ot attn=CPU -nkvo
Could go q4_0 kv cache for lower VRAM usage!

12.733 GiB (4.050 BPW)
f32:  373 tensors
q4_0:   62 tensors blk.*.attn_v.weight
q8_0:    1 tensors token_embd.weight
iq3_k:  124 tensors ffn_(gate|up).weight
type iq4_ks:  248 tensors ffn_down.weight
Final estimate: PPL = 8.2367 +/- 0.06329

Big Thanks

Shout out to Wendell and the Level1Techs crew, the community Forums, YouTube Channel! BIG thanks for providing BIG hardware expertise and access to run these experiments and make these great quants available to the community!!!

Also thanks to all the folks in the quanting and inferencing community here and on r/LocalLLaMA for tips and tricks helping each other run all the fun new models!

Excited to share and learn together. Thanks!

References

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