RTX 5070 Ti Challenges RTX 3090 VRAM

Hot take: for local LLMs, VRAM still matters more than headline tensor TFLOPS, so the 3090 is usually the safer pure-inference buy unless your models are comfortably small.

• –The 5070 Ti’s raw tensor numbers are impressive on paper, but most local inference stacks do not translate those peaks into linear real-world gains.
• –In practice, llama.cpp and similar runtimes still lean heavily on custom CUDA kernels, quantization format support, memory bandwidth, and VRAM capacity.
• –The 3090’s 24GB gives more room for 27B-class models, larger contexts, and fewer CPU offload compromises.
• –A 16GB 5070 Ti is likely faster for workloads that fully fit in memory, but it is more constrained once model size, KV cache, and vision components are involved.
• –Two 5070 Ti cards do not behave like one big 32GB card; multi-GPU inference adds software complexity and usually scales imperfectly.
• –Best fit: 3090 for maximum flexibility in local LLM inference; 5070 Ti only if you prioritize efficiency and mostly run smaller models.