Home / Compare / NVIDIA RTX 4080 16 GB vs NVIDIA RTX 4070 Ti SUPER 16 GB
NVIDIA RTX 4080 16 GB vs NVIDIA RTX 4070 Ti SUPER 16 GB for local AI
Canonical GPU chips, not ASUS vs MSI coolers. Memory fit uses calculator v1.0.0. Speed uses published llama.cpp estimates only. Query states (model / quant / context) are not indexed.
Decision dimensions
These are facts from the existing memory calculator, performance cache, and Amazon summary. This page does not pick a winner.
| NVIDIA RTX 4080 16 GB | NVIDIA RTX 4070 Ti SUPER 16 GB | |
|---|---|---|
| Advertised VRAM | 16 GB | 16 GB |
| Usable VRAM (calculator 90%) | 14.4 GB | 14.4 GB |
| Architecture | Ada Lovelace | Ada Lovelace |
| Memory | GDDR6X · 716.8 GB/s · 256-bit | GDDR6X · 672.3 GB/s · 256-bit |
| Models fully fitting at Q4 / 8K | 37 | 37 |
| Limited context / offload / does not fit | 1 / 10 / 7 | 1 / 10 / 7 |
| llama.cpp short-context coverage | available | available |
| Mapped / Amazon-matched SKUs | 13 / 10 | 12 / 7 |
| Lowest fresh matched Amazon card | $859.99 Check price | $899.99 Check price |
| Current price per advertised VRAM GB | $54 / GB | $56 / GB |
Prices are the lowest fresh Amazon-matched board-partner SKU in the last 24 hours, not MSRP. Price per GB is that price divided by advertised VRAM — not a value score.
Difference in those current lowest fresh cards: $-40.00 (-4%).
Workload
Runtime is llama.cpp CUDA — the only runtime with published Phase 5 estimates. Changing the model does not create a new indexable URL.
What each GPU uniquely fits
Full VRAM fit at Q4 / 8K among published calculation-supported models. Identical coverage is reported as such — it is not turned into a winner.
Both GPUs fully fit the same currently supported model set at Q4 / 8K (37 models).
37 models fit fully on both · examples: StarCoder2 15B, Qwen3 14B, DeepSeek R1 Distill Qwen 14B, Phi-4, Qwen2.5 14B Instruct, Phi-3 Medium 128K Instruct, Mistral Nemo 12B Instruct, Falcon 3 10B Instruct.
llama.cpp performance
Reuses Performance Model v1.0.0 published rows only. Short-context llama-bench pp512/tg128. Not 8K–128K speed. Methodology
Select a model to load published decode estimates for the same workload on both GPUs.
What this comparison shows
- Memory capacity: both advertise 16 GB.
- Model fit: both fully fit the same number of currently supported catalog models (37) at Q4 / 8K.
- Performance: No published llama.cpp performance estimate for either GPU.
- Current commerce: lowest fresh matched NVIDIA RTX 4080 16 GB SKU $859.99; NVIDIA RTX 4070 Ti SUPER 16 GB $899.99.
Board power / TDP is not compared: RigForAI does not yet have a single normalized watt metric. Energy per token is out of scope. This is not a best-GPU ranking.