Ollama Users Eye Cheap Dual-GPU VRAM

The instinct is sound for local inference: more VRAM usually matters more than raw speed once models stop fitting comfortably. The catch is that dual-GPU rigs are only useful if your software stack can address them cleanly, and that is not the same thing as magically pooling memory.

• –Ollama officially supports NVIDIA GPUs and lets you target specific cards with `CUDA_VISIBLE_DEVICES`, so a second NVIDIA board is at least operationally plausible.
• –That still does not mean the two 16GB pools behave like one 32GB pool; in practice, multi-GPU support is about workload placement and model sharding, not seamless memory merging.
• –Mixing NVIDIA with AMD or Intel is not a simple “add VRAM” move. Ollama uses separate backends for CUDA, ROCm, Metal, and experimental Vulkan, which points to backend-specific use rather than one unified cross-vendor VRAM stack.
• –A used 16GB add-in card can be a cheaper bridge than jumping to a 24GB-plus flagship, but it comes with extra power, cooling, and software-tuning overhead.
• –If the goal is fewer headaches, a single larger-VRAM card is usually the cleaner route; if the goal is cheapest incremental capacity, the second NVIDIA card is the more realistic experiment.