Verified quants only. Sub-4-bit variants are excluded from this release because they produce degenerate output on this model size — no point shipping broken files.

GGUF imatrix builds of google/gemma-4-31B-it.

Built with llama.cpp and importance-matrix calibration on a public multilingual + code + math corpus. Every quant is loaded, prompted, and visually checked before publication.

👋 From the author

Hi local LLM enthusiasts! You may have noticed a few quants from me over the past months — those were one-off experiments. This release is the first one from a fully automated pipeline.

Please try them out, share feedback, and let me know which quants you find useful or which ones you wish I had made. For now, anything below 4-bit produces degraded output on this architecture, so I am holding off on those until they actually work well. More quants and more models coming.

🎯 Pick a quant

Quant	Size	What it's for
`Q8_0`	31G	Almost the original. Pick this if RAM isn't a concern.
`Q6_K_L`	24G	Near-lossless with Q8_0 embeddings. Best of the K-quants.
`Q6_K`	24G	Near-lossless. Excellent fidelity at smaller size than Q8_0.
`Q5_K_L`	21G	Q5_K_M with Q8_0 embeddings. High quality, small overhead.
`Q5_K_M`	21G	Sweet spot between Q4 and Q6. Solid all-rounder.
`Q5_K_S`	20G	Slightly smaller than Q5_K_M, virtually identical output.
`Q4_K_L`	18G	Q4_K_M with Q8_0 embeddings. The smart 4-bit choice.
`Q4_K_M`	18G	The default. Most-downloaded quant for a reason.
`Q4_K_S`	17G	A bit smaller than Q4_K_M, a tiny step down.
`IQ4_NL`	17G	ARM-optimized 4-bit. For Raspberry Pi & friends.
`IQ4_XS`	16G	Tightest 4-bit format. Quality close to Q4_K_S, smaller.

*_L variants override the output tensor and embeddings to Q8_0 — small disk cost, better output stability.

Tl;dr:

🟢 Q4_K_M — start here unless you have a reason not to
🟡 Q5_K_M — small quality bump if you have RAM to spare
🔵 Q8_0 — pick this if you're not RAM-constrained and want max quality
🟠 IQ4_XS — when 4-bit is too big
🟣 IQ4_NL — running on ARM (Pi, phones)

💬 Prompt format

<bos><start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>model

📥 Download

Single file (recommended):

bash

hf download Krasnopjorovs/gemma-4-31b-it-Imatrix-GGUF \
  --include "gemma-4-31b-it-Q4_K_M.gguf" --local-dir .

Whole repo:

hf download Krasnopjorovs/gemma-4-31b-it-Imatrix-GGUF --local-dir ./gemma-4-31b-it-gguf

▶ Run

bash

./llama-server \
  -m gemma-4-31b-it-Q4_K_M.gguf \
  -c 32768 -ngl 99 \
  --host 0.0.0.0 --port 8080

Then point your favorite chat client at http://localhost:8080/v1.

🔬 Calibration

Imatrix generated from reapmix — community calibration mix (~400K tokens, multilingual + code + math). Same class of public calibration data used by other community publishers; this release makes no unique calibration claim.

The output tensor and embeddings carry disproportionate weight in quantized output. The *_L variants keep these at Q8_0 — small disk cost for noticeably better stability at low bit-rates.

✅ Build info

Source: google/gemma-4-31B-it
llama.cpp: latest mainline
Quantization: CPU
Imatrix calibration: GPU
Generated: 2026-06-06

🙏 Credits

Source model — Google (Apache 2.0)
Calibration corpus — reapmix by eaddario
Quantization tooling — llama.cpp