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.
AI Architect Verdict
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)
VRAM: 16 GB GDDR6X
Memory Bus: 256-bit (672 GB/s)
Typical Price: ~$799 (New)
Inference Speed: ~45 tok/s (14B Q4)
NVIDIA RTX 4080 Super (16GB)
VRAM: 16 GB GDDR6X
Memory Bus: 256-bit (736 GB/s)
Typical Price: ~$999 (New)
Inference Speed: ~52 tok/s (14B Q4)
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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