Using Gemma 4 E4B with the LiteRT engine - ~2.4x speedup over Q4 GGUF in text generation, image processing roughly the same
A user has achieved a 2.4x speedup in text generation using Google's Gemma 4 E4B model by employing the LiteRT engine with multi-token prediction (MTP). This optimization significantly outperforms the standard Q4 GGUF quantization in llama.cpp for text-based tasks. However, for image captioning, the speed improvement was only marginal (1.1x) because the vision encoder, not the text decoder, was the bottleneck. The user has created a Python wrapper to provide an OpenAI-compatible endpoint for this faster local model, integrating it into their workflow. AI
IMPACT Demonstrates significant local inference speedups for open-source models, potentially lowering barriers to advanced AI use.