The optimization took just a few days. According to Google, it achieved 100% token parity with the original implementation without degrading output quality.
Benchmark Results
| Metric | Before | After |
| Model | Gemma 4 E2B | Gemma 4 E2B |
| GPU | NVIDIA A100 | NVIDIA A100 |
| Framework | vLLM | vLLM |
| Throughput | ~37,900 tokens/s | 108,000+ tokens/s |
| Speedup | 1.0x | 2.85x |
| Token parity | Reference | 100% |
| Quality degradation | — | None reported |
These figures represent aggregate inference throughput, not single-user token generation speed.
Gemma 4 E2B Technical Specifications
- Effective parameters: 2.3 billion
- Total parameters: 5.1 billion
- Context window: 128K tokens
- Architecture: Hybrid local/global attention
- Transformer layers: 35
- Vocabulary size: 262K tokens
- Input modalities: Text, images, audio, video
- Output modality: Text
- Features: Function calling, speculative decoding
The optimization targeted attention processing and speculative decoding, which allows a smaller draft model to propose tokens for verification by the main model.
Gemma 4 E2B vs. Gemma 3n E2B
| Specification | Gemma 3n E2B | Gemma 4 E2B |
| Effective parameters | ~1.91B | 2.3B |
| Context window | 32K | 128K |
| Text and image input | Yes | Yes |
| Audio support | Yes | Yes |
| Native video input | No | Yes |
| Native function calling | No | Yes |
| Dedicated speculative decoding model | Not documented | Supported |
Gemma 4 E2B offers a fourfold increase in context length, expanded multimodal capabilities, and native tool support.
Important: The reported 2.85x speedup compares optimized and unoptimized Gemma 4 E2B inference, not Gemma 4 against Gemma 3n.
Performance and Quality
Google reports:
100% token parity. Zero quality drop.
This means the optimized implementation generated identical output tokens to the reference implementation in Google's validation tests.
However, the complete benchmark configuration, including batch size, request concurrency, and numerical precision, was not disclosed.
Conclusion
Gemini 4 Argon increased Gemma 4 E2B inference throughput on NVIDIA A100 by approximately 185%, without modifying the model weights.
The optimization was completed in days rather than months, demonstrating how AI-assisted engineering can substantially improve inference performance on existing GPU hardware.
