gemma-4-e2b-it-Q8_0-GGUF

Gemma 4 E2B it — Q8 GGUF

8-bit quantized GGUF version of google/gemma-4-e2b-it.
Highest quality quantization — 96% Top-1 agreement with F16, effectively lossless.

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 · Q6_K

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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: 37.11 dB SQNR and KL divergence of just 0.017 — for all practical purposes, identical to F16
• Top-1 Agreement: 96.0% — the model picks the same token as F16 96 times out of 100
• Speed: 16.2 tok/s — still 2.9× faster than F16, just slower than lower-bit quants
• Size: 4.63 GB — fits in 6 GB RAM; half the size of F16
• vs Q6_K: +8.4 dB SQNR and +1.9% Top-1 for 1.05 GB extra; worth it if you have the RAM
• Best for: Maximum quality with reasonable size reduction; production deployments where output must match F16 as closely as possible

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Usage

# llama.cpp CLI
./llama-cli -m gemma-4-e2b-q8.gguf -p "Explain how a transformer neural network works." -n 200

# llama-cpp-python
from llama_cpp import Llama

llm = Llama(model_path="gemma-4-e2b-q8.gguf", n_ctx=2048)
output = llm("Explain how a transformer neural network works.", max_tokens=200)
print(output["choices"][0]["text"])

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Hardware

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