dahus/gemma-4-e2b-Q6_K-GGUF

Gemma 4 E2B — Q6_K GGUF

6-bit quantized GGUF version of google/gemma-4-e2b-it.
Near-lossless quantization — 94% Top-1 agreement with F16 at a fraction of the size.

Other quantizations in this series:
Q2_K · Q3_K_S · Q3_K_M · Q4_K_S · Q4_K_M · Q5_K_S · Q5_K_M · Q8

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File Info

Property	Value
Format	GGUF Q6_K
File size	3.58 GB
Bits per weight	~6
Size vs F16	2.4× smaller

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Benchmark Results

Tested across 4 categories (Math, Logic, Code, Science), 3 prompts each.
Greedy decoding, 200 max new tokens. Metrics compare logit distributions vs F16 baseline.

Results by Category

Category	Speed (tok/s)	SQNR	Top-1 Agreement	KL Divergence
🔢 Math	19.7	28.7 dB	94.3%	0.0796
🧠 Logic	19.9	29.3 dB	93.2%	0.0891
💻 Code	20.0	29.0 dB	93.6%	0.0502
🔬 Science	19.9	27.8 dB	95.1%	0.0784
Overall	19.9	28.72 dB	94.1%	0.0743

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Quantization Comparison

Model	Size	Speed (tok/s)	vs F16 speed	SQNR	Top-1 Agree	KL Div
F16 (baseline)	8.67 GB	5.7	1.0×	baseline	baseline	baseline
Q4_K_M	3.19 GB	24.0	4.2×	20.33 dB	82.4%	0.3356
Q5_K_M	3.38 GB	22.0	3.9×	23.25 dB	86.9%	0.1248
Q6_K (this)	3.58 GB	19.9	3.5×	28.72 dB	94.1%	0.0743
Q8	4.63 GB	16.2	2.9×	37.11 dB	96.0%	0.0171

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Key Findings

• Quality: 94.1% Top-1 agreement — crosses the "near-identical to F16" threshold; only Q8 is better
• SQNR: 28.72 dB — a substantial 5.5 dB jump over Q5_K_M; outputs are essentially indistinguishable from F16 in practice
• Speed: 19.9 tok/s — 3.5× faster than F16
• Size: 3.58 GB — only 1 GB more than Q4_K_M for a major quality improvement
• vs Q8: Q6_K is 1.05 GB smaller and 3.7 tok/s faster, with only a small quality difference (94.1 vs 96.0% Top-1)
• Best for: Quality-sensitive tasks where you want near-F16 fidelity but still need to fit under ~5 GB; scientific explanation, precise reasoning, complex multi-step code

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Usage

# llama.cpp CLI
./llama-cli -m gemma-4-e2b-q6k.gguf -p "Explain the difference between supervised and reinforcement learning." -n 200

# llama-cpp-python
from llama_cpp import Llama

llm = Llama(model_path="gemma-4-e2b-q6k.gguf", n_ctx=2048)
output = llm("Explain the difference between supervised and reinforcement learning.", max_tokens=200)
print(output["choices"][0]["text"])

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Hardware

Tested on: CPU inference (llama.cpp)
Context: 2048 tokens | Greedy decoding