gemma-4-e2b-it-Q3_K_M-GGUF

Gemma 4 E2B it — Q3_K_M GGUF

3-bit medium quantized GGUF version of google/gemma-4-e2b-it.
Usable quality at very small size — a step up from Q2_K with only 0.2 GB more.

Other quantizations in this series:
Q2_K · Q3_K_S · Q4_K_S · Q4_K_M · Q5_K_S · Q5_K_M · Q6_K · Q8

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File Info

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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

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Quantization Comparison

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Key Findings

• Quality: 63.2% Top-1 agreement — coherent outputs, but noticeably different from F16 on complex prompts
• Speed: 27.4 tok/s — 4.8× faster than F16
• Size: 2.98 GB — fits under 4 GB RAM with room to spare
• vs Q3_K_S: Q3_K_M has higher SQNR (13.93 vs 10.12 dB) but higher KL divergence (1.67 vs 1.26); Q3_K_S is slightly faster. In practice, quality is similar.
• Best for: Low-RAM devices where Q4 variants don't fit; simple chat and factual Q&A

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Usage

# llama.cpp CLI
./llama-cli -m gemma-4-e2b-q3km.gguf -p "Explain the water cycle." -n 200

# llama-cpp-python
from llama_cpp import Llama

llm = Llama(model_path="gemma-4-e2b-q3km.gguf", n_ctx=2048)
output = llm("Explain the water cycle.", max_tokens=200)
print(output["choices"][0]["text"])

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Hardware

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