Gemma 4 Local Inference: Ollama Benchmarks, llama.cpp KV Cache Fix, NPU Deployments

This article covers advancements in local inference for the Gemma 4 language model, including a fix for the VRAM-consuming KV cache issue in llama.cpp, and benchmarks for running Gemma 4 on consumer GPUs with different quantization levels using Ollama.

Details

The article discusses several advancements in local inference for the Gemma 4 language model. First, the llama.cpp project released an update that addressed the VRAM consumption and performance issues previously observed with the Gemma 4 KV cache implementation. This fix significantly reduces the memory footprint, enabling users to run larger Gemma 4 models more efficiently on consumer-grade GPUs. The article also highlights a community member's success in deploying Gemma 4 26B with A4B (4-bit) quantization on a Rockchip NPU, using a custom llama.cpp fork. This demonstrates the growing capability of running open-weight models on energy-efficient, non-GPU hardware like NPUs, which is ideal for edge computing and low-power devices. Finally, the article covers detailed benchmarks for running the Gemma 4:31B model using Ollama on an NVIDIA RTX 3090 GPU. The benchmarks compare performance across full precision (FP), 8-bit (Q8), and 4-bit (Q4) quantization levels, providing valuable insights for users aiming to balance model accuracy with VRAM constraints and inference speed.