Gemma 4 26B hits sweet spot for 24GB GPUs

Running powerful reasoning models locally is becoming highly accessible thanks to optimized MoE architectures.

• –The 26B-A4B MoE model activates only ~4B parameters per token, leaving ample VRAM for context while maintaining high inference speed.
• –Configuring Sliding Window Attention (SWA) and strictly limiting parallel slots is critical to avoid out-of-memory errors on 24GB cards.
• –Developers are increasingly pairing these efficient local inference setups with tools like Claude Code or OpenClaw for completely private AI coding workflows.