NVIDIA RTX 4070 Ti Super vs RTX 4080 Super for Local LLMs
Exact hardware breakdown, VRAM sizing, tokens/second inference throughput, and truthful total cost-of-ownership math.
RTX 4070 Ti Super Offers Superior Value-per-GB at the Same 16GB VRAM Tier
Both cards share the exact same 16GB VRAM capacity on a 256-bit memory bus. For local LLM inference, memory capacity dictates which models fit. Both cards run 14B models at FP16 or 32B models at aggressive 3-bit quantization, with the 4080 Super offering ~10% faster bandwidth. However, saving $200 on the 4070 Ti Super is mathematically wiser, as neither card can fit 70B models without CPU offload.
NVIDIA RTX 4070 Ti Super (16GB)
NVIDIA RTX 4080 Super (16GB)
Popular AI Model Fit Comparison
| AI Model | VRAM Req (8k) | NVIDIA RTX 4070 Ti Super | NVIDIA RTX 4080 Super |
|---|---|---|---|
| Qwen 2.5 14B Instruct (Q8_0) | 18.2 GB | Exceeds VRAM (OOM) | Exceeds VRAM (OOM) |
| Qwen 2.5 14B Instruct (Q4_K_M) | 10.9 GB | Runs Comfortably (+5.1GB) | Runs Comfortably (+5.1GB) |
| Meta LLaMA 3.2 11B Vision | 10.5 GB | Runs Comfortably (+5.5GB) | Runs Comfortably (+5.5GB) |
| Meta LLaMA 3.1 8B (FP16) | 19.4 GB | Exceeds VRAM (OOM) | Exceeds VRAM (OOM) |
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