gemma-4-e2b-it-Q4_K_M-GGUF

Gemma 4 E2B it — Q4_K_M GGUF

4-bit medium quantized GGUF version of google/gemma-4-e2b-it.
Recommended default — best balance of quality, speed, and size in the series.

Other quantizations in this series:
Q2_K · Q3_K_S · Q3_K_M · Q4_K_S · 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: 82.4% Top-1 agreement — large jump from Q3 (63%), outputs are reliable and coherent
• Speed: 24.0 tok/s — 4.2× faster than F16
• Size: 3.19 GB — fits in 4 GB RAM
• vs Q4_K_S: Q4_K_M is marginally better quality (20.33 vs 19.10 dB SQNR, 82.4 vs 80.9% Top-1) for only 60 MB more
• Best for: General-purpose use — math, code, Q&A, chat; the go-to choice when you don't have a specific reason to pick another variant

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Usage

# llama.cpp CLI
./llama-cli -m gemma-4-e2b-q4km.gguf -p "Write a Python function for binary search." -n 200

# llama-cpp-python
from llama_cpp import Llama

llm = Llama(model_path="gemma-4-e2b-q4km.gguf", n_ctx=2048)
output = llm("Write a Python function for binary search.", max_tokens=200)
print(output["choices"][0]["text"])

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Hardware

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