LocalLLaMA weighs used 3090s for Gemma 4

This is the classic local-LLM reality check: VRAM, software support, and context headroom matter more than raw spec-sheet excitement. For a first serious Gemma 4 box, a used 3090 is still the pragmatic answer even if it stretches the budget.

• –Google’s Gemma 4 announcement positions the 26B MoE and 31B dense models as local-capable on consumer GPUs when quantized, but 24GB cards will still feel tight once you factor in long context and KV cache.
• –Used RTX 3090s remain the safest bet because CUDA support is mature across Ollama, llama.cpp, vLLM, and the rest of the local inference stack.
• –AMD’s 7900 XTX is the cleanest fallback if you want 24GB and better availability, but ROCm support is still less frictionless than Nvidia for hobbyist local LLM setups.
• –Intel’s Arc Pro B70 looks compelling on paper with 32GB and vGPU/SR-IOV support, but the ecosystem is still immature enough that it’s a riskier starter card.
• –The server-side constraints matter here too: PCIe 3.0, Windows VM passthrough, and SolidWorks/RDP usage all push this toward a “works reliably” choice over a “best theoretical value” choice.