RTX 3090 could extend local LLM headroom

This is less a product story than a real-world local AI infrastructure tradeoff: a second GPU helps capacity more than throughput.

• –In text-generation-webui, mixed 4090 + 3090 setups are most useful when you need to fit larger quantized models across more VRAM
• –Single-stream inference usually does not scale cleanly across two consumer GPUs because of model sharding overhead and PCIe transfer costs
• –The extra 3090 is more compelling for batch jobs, parallel sessions, or experimentation than for making one chat session feel dramatically faster
• –The thread reflects a common local LLM reality: once you already own high-end GPUs, memory limits matter more than raw FLOPS
• –For AI developers, this is solid evidence that workstation design for local inference is increasingly about capacity planning, not just buying the fastest card