Gemma 4 26B on a consumer GPU: build pain, throughput, and BFCL numbers (algollabs.com)

Google's Gemma 4 26B model has been successfully utilized on a consumer-grade workstation powered by an NVIDIA RTX 5070 Ti. This deployment, without cloud use or API restrictions, demonstrated noteworthy performance metrics: it achieved a prompt processing speed of 5,951 tokens per second and 137.7 tokens per second for generation. With only 3.8 billion of its 26 billion parameters active per pass due to its mixture-of-experts architecture, the model efficiently fits within the GPU's 16 GB memory. This setup challenges the prevailing notion that substantial AI work necessitates high-end hardware or API access, as users can now leverage competitive capabilities on affordable consumer hardware. The significance of this milestone lies in its implications for accessibility and efficiency in AI/ML applications. The quantitative evidence gathered, including the model's accuracy scores on the Berkeley Function Calling Leaderboard, shows performance that rivals existing high-cost, cloud-based solutions. The findings illustrate that for small businesses or privacy-conscious projects, investing in a robust local setup can yield significant benefits—in both performance and cost—while meeting demanding workloads. These advancements foreshadow a shift in how AI capabilities are accessed and implemented, heralding a new era of democratized AI development.