Gemma 4 runs locally on consumer hardware with 86.4% tool-calling accuracy, rivaling cloud models in practical coding workflows. A developer compared the 26B MoE variant on a 24 GB MacBook Pro and 31B Dense variant on a Dell Pro Max GB10 against GPT-5.4 via Codex CLI, finding local inference viable despite slower token speeds. The Mac setup required precise quantization flags (-ctk q8_0 -ctv q8_0) and llama.cpp configuration to bypass Ollama's streaming bugs, while the GB10 used Ollama v0.20.5 with CUDA-optimized builds.

The test revealed surprising tradeoffs: the GB10's 10 tokens/second throughput generated cleaner code with fewer iterations, while the Mac's 52 tokens/second speed came at the cost of debug-heavy outputs. Both local models produced passing tests versus GPT-5.4's 65-second benchmark, though cloud models retained edge in code quality. The Mixture of Experts architecture enabled unexpected efficiency gains on Apple Silicon, with sparse activation reducing memory demands despite lower raw compute power.

Key setup lessons include disabling web_search in Codex CLI profiles and using direct GGUF paths (-m flag) to avoid OOM crashes. While local models demand meticulous configuration, the study validates their use for privacy-sensitive or cost-conscious workflows. Hybrid approaches - local for iteration, cloud for complexity - emerged as pragmatic solutions.

Mixture of Experts, local inference, and tool-calling accuracy proved decisive factors in balancing speed, cost, and reliability.