NVIDIA RTX 4070 Ti Super - 16GB usable
Specifications
| Brand | NVIDIA |
|---|---|
| Model | RTX 4070 Ti Super |
| Usable VRAM | 16GB |
| Architecture | Ada |
| CUDA / Stream Processors | 8,448 |
| Memory Bandwidth | 672 GB/s |
| TDP | 285W |
| FP32 TFLOPS | 44 |
Current Offers
Used from $950New from $1,599
Prices last updated:
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Lowest tracked offers by retailer
US · USD · Weekly since May 2026 · Shipping may be extra
About these prices
Each line is the lowest offer we tracked from that retailer at weekly snapshots, ending at today's offer. The condition can vary between snapshots (new, used, or unrecorded for older ones). eBay prices exclude shipping. Tax is excluded. Amazon shows a current price only.
Weekly offers table
| Date | eBay | Amazon | Newegg |
|---|---|---|---|
| Today, Nov 3, 2026 | $949.97 | $1,099.99 | – |
| Oct 30, 2026 | $949.97 | – | – |
| Oct 23, 2026 | $912.99 | – | – |
| Oct 9, 2026 | $900.00 | – | – |
| Oct 2, 2026 | $899.99 | – | $1,323.52 |
| Sep 25, 2026 | $899.00 | – | $1,316.26 |
| Sep 18, 2026 | $898.00 | – | $1,308.53 |
| Sep 11, 2026 | $815.00 | – | $1,260.99 |
| Sep 4, 2026 | $799.99 | – | $1,259.52 |
| Sep 1, 2026 | $799.99 | – | $1,301.74 |
| Aug 28, 2026 | $789.99 | – | $1,301.74 |
| Aug 21, 2026 | $779.99 | – | $1,317.00 |
| Aug 14, 2026 | $779.99 | – | $1,304.00 |
| Aug 7, 2026 | $750.00 | – | $1,308.53 |
| Jul 31, 2026 | $749.99 | – | $1,133.60 |
| Jul 24, 2026 | $679.99 | – | $1,184.37 |
| Jul 17, 2026 | $748.97 | – | $1,184.37 |
| Jul 10, 2026 | $700.00 | – | $1,049.00 |
| Jul 3, 2026 | $725.00 | – | $1,189.00 |
| Jun 26, 2026 | $730.00 | – | $1,189.00 |
| Jun 19, 2026 | $737.00 | – | $1,221.67 |
| Jun 12, 2026 | $750.00 | – | $1,221.67 |
| Jun 5, 2026 | $739.99 | – | $1,131.05 |
| May 29, 2026 | $779.99 | – | $1,301.78 |
| May 22, 2026 | $724.99 | – | $1,235.79 |
| May 15, 2026 | $749.00 | – | $1,235.79 |
| May 8, 2026 | $729.00 | – | $1,235.79 |
For AI / LLM Use
Good for 14B models. 30B requires aggressive quantization.
What Models Can It Run?
- 14B Q6_K, 30B Q3_K (tight)
- 14B Q4_K_M, 7B full precision
- 7B Q6_K, 14B Q3_K (tight)
- 7B Q4_K_M only
Estimated Performance
Generation: ~50 tokens/sec
Prefill: ~786 tokens/sec
Recommended Quantisations
- Q4_K_M for 14B models
- Q6_K for 7B-8B models
- Q8 for 7B if VRAM allows
Pros & Cons
Pros
- Ada architecture: broad CUDA software support
- Consumer card: easy to install, display output
Cons
- 16GB usable VRAM: may need quantization for 30B+ models
- Moderate memory bandwidth: not the fastest for inference
Community Verdict
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