gemma-4-E4B-it Offline on PC For Low VRAM (6GB/8GB)

The most rapid route to a local installation of this model is through Docker.

Make sure to follow the instructions below.

The loader auto-caches the model archive (several GBs included).

The installer will automatically analyze your hardware and select the optimal configuration for your system.

💾 File hash: 7a716e362ce0633af57d814c07856b19 (Update date: 2026-06-22)



  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • Storage:100 GB free space for HuggingFace cache folder
  • Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

Gemma-4-E4B-it is a state‑of‑the‑art language model engineered for high‑efficiency inference on edge devices. It incorporates 2 B parameters and a 4 K context window, allowing nuanced comprehension while preserving low latency. The architecture leverages advanced quantization techniques to achieve sub‑2 ms token generation on consumer hardware. Its design includes multi‑head attention and grouped‑query attention, delivering strong performance across benchmarks such as MMLU and GSM‑8K. The model also supports seamless integration with developer tools through its open‑source API.

Parameters 2 B
Context Length 4 K tokens
Quantization INT4
Throughput >2000 tokens/s on GPU
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