Google Gemma 4 And TurboQuant Explained For Web Developers
On this page
- Why Gemma 4 Matters
- The Gemma 4 Model Family
- Architectural Innovations
- Alternating Attention
- Dual RoPE
- Per-Layer Embeddings (PLE)
- Shared KV Cache
- Understanding The KV Cache Problem
- TurboQuant: 3-Bit KV Cache With Zero Accuracy Loss
- Stage 1: PolarQuant
- Stage 2: QJL (Quantized Johnson-Lindenstrauss)
- The Numbers: Before And After TurboQuant
- Gemma 4 + TurboQuant In Practice
- MLX on Apple Silicon
- Llama.cpp
- Transformers (Python)
- Edge Deployment
- When To Use Which Model
- Benchmarks In Context
- Key Takeaways
- Resources
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