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// 70d agoBENCHMARK RESULT
RTX 5070 Ti Challenges RTX 3090 VRAM
This Reddit post asks whether a used RTX 3090 or a new RTX 5070 Ti is the better buy for local LLM inference, especially in llama.cpp-style workloads. The debate centers on whether the 5070 Ti’s newer tensor cores and much higher peak FP4/FP8 throughput can outweigh the 3090’s 24GB of VRAM, which is still attractive for larger models and longer contexts.
// ANALYSIS
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.
// TAGS
nvidiagpullmlocal-inferencellama.cpptensor-coresquantizationvramblackwellampere
DISCOVERED
70d ago
2026-04-01
PUBLISHED
70d ago
2026-03-31
RELEVANCE
8/ 10
AUTHOR
robkered