Gemma 4 26B Can Run on a 13-Year-Old Xeon CPU Using Quantization

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A technical tutorial published on tamiz.pro shows how Google's Gemma 4 26B large language model can be run on legacy Intel Xeon E5 v2-series processors without any GPU. The approach relies on 4-bit model quantization, which cuts RAM requirements from roughly 120GB down to about 40GB, making the setup feasible on servers with at least 64GB of memory. Using CPU-specific optimizations in PyTorch and Hugging Face Transformers, the configuration achieves around 12 tokens per second on the aging hardware. While modern GPUs deliver 100–300 tokens per second, this method operates at approximately 15% of typical GPU costs. The author recommends the setup for edge deployments or proof-of-concept work, while suggesting newer Xeon Scalable processors for production-grade workloads.

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