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) |
Silicon Architecture, Bandwidth & Thermal Realities
Unlike the original non-Super RTX 4070 Ti (which shipped with a crippled 192-bit 12GB AD104 die), the RTX 4070 Ti Super steps up to the exact same AD103 silicon family used by the RTX 4080 Super. Both cards wire eight 32-bit GDDR6X memory controllers into a full 256-bit interface; the only bandwidth difference comes from 21 Gbps memory clocks (672 GB/s) on the 4070 Ti Super versus 23 Gbps modules (736 GB/s) on the 4080 Super. In real-world Ollama and vLLM benchmarks on Qwen 2.5 14B, Gemma 4 12B, and OpenAI gpt-oss 20B (`14.4 GB` Q4), both GPUs hit identical context ceilings.
On either 16GB card, you can run OpenAI `gpt-oss-20b` (20.9B MoE) or Mistral Small 24B locally without OOM by selecting `IQ4_XS` weights plus `--kv-cache-dtype fp8` / `-ctk q8_0 -ctv q8_0`, trimming total allocation under 15.1 GB.
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Frequently Asked Questions
Can an RTX 4070 Ti Super or 4080 Super run 32B coding models?
At standard Q4_K_M, 32B dense models require ~21.6 GB VRAM and will spill ~5.6 GB over PCIe into system RAM. To stay inside 16GB VRAM, use Q3_K_M or run a 20B–26B sparse MoE model such as `gpt-oss-20b` or `gemma-4-26b-a4b` at IQ4_XS.
How does RTX 4070 Ti Super compare against RTX 4060 Ti 16GB?
While both have 16GB VRAM, the 4070 Ti Super has 2.33x higher memory bandwidth (672 GB/s vs 288 GB/s on the 128-bit 4060 Ti 16GB), generating tokens more than twice as fast.
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