Gemma 4 Sparks Local-LLM Hardware Questions

The post reflects the current inflection point for local LLMs: model quality is getting good enough that hardware choice is now mostly about VRAM, quantization, and runtime support rather than raw specs alone.

• –Gemma 4 is positioned as an open model family built for hardware-constrained deployment, with 2B/4B edge variants and larger 26B/31B models for stronger offline reasoning.
• –For a 16GB RX 6800 XT, the practical path is likely quantized smaller Gemma 4 variants first; the 31B-class models are much more demanding and will usually trade speed, context, or quality to fit.
• –AMD is not a dead end for local inference, but the stack is less uniform than NVIDIA: ROCm, vLLM, and llama.cpp support exist, yet model/vendor compatibility can be more finicky and Linux-oriented in practice.
• –The 2070 Super still has utility as a personal experimentation card, but 8GB VRAM is the real ceiling; the RX 6800 XT is the better “one GPU for LLMs” upgrade if the user wants years of headroom.
• –The most important advice for this user is to optimize for a workflow stack first, not just a chip: choose the model size and runtime that match the GPU you can actually run reliably.