gemma-4-E4B-it-MLX-4bit with 1M Context

Posted byakroll222Posted inFrontends

gemma-4-E4B-it-MLX-4bit with 1M Context

Running this model locally is fastest when deployed through Docker.

Use the instructions provided below to complete the setup.

Hands-free setup: the system self-downloads the heavy model files.

To guarantee smooth performance, the installation process auto-selects the best possible options for your PC.

📤 Release Hash: d12a21706b853c4ef5bfe4efca564688 • 📅 Date: 2026-06-26
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  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • Disk Space:70 GB free space for full FP16 weights storage
  • Graphics: 12 GB VRAM minimum required for basic quantization

The **gemma-4-E4B-it-MLX-4bit** model represents a significant advancement in open‑source language models, combining the gemma architecture with MLX optimization for ultra‑low latency inference. Built on a 4‑bit quantized backbone, it delivers high performance while consuming only a few megabytes of memory, making it ideal for edge devices and mobile applications. With **4.5 B** parameters and a context window of 8K tokens, the model balances accuracy and efficiency, achieving state‑of‑the‑art results on benchmark suites. The integrated MLX compiler further accelerates inference by optimizing kernel execution and reducing overhead, resulting in sub‑10ms response times on consumer hardware. Below is a quick comparison of key specifications that highlight why this model stands out in the current landscape.

Parameters 4.5 B
Quantization 4‑bit
Context Length 8K tokens
Inference Speed <10 ms
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