NVIDIA Titan RTX - 24GB usable
Specifications
| Brand | NVIDIA |
|---|---|
| Model | Titan RTX |
| Usable VRAM | 24GB |
| Architecture | Turing |
| CUDA / Stream Processors | 4,608 |
| Memory Bandwidth | 672 GB/s |
| TDP | 280W |
| FP32 TFLOPS | 16.3 |
Current Offers
No fresh used offer today · recently £650 on eBay
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Lowest tracked offers by retailer
UK · GBP · Weekly since Aug 2026 · Shipping may be extra
- eBay (no current offer)
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.
Weekly offers table
| Date | eBay |
|---|---|
| Oct 23, 2026 | £650.00 |
| Oct 9, 2026 | £650.00 |
| Oct 2, 2026 | £814.00 |
| Sep 25, 2026 | £675.00 |
| Sep 18, 2026 | £1,200.00 |
| Sep 11, 2026 | £1,200.00 |
| Sep 4, 2026 | £1,200.00 |
| Sep 1, 2026 | £1,200.00 |
| Aug 28, 2026 | £1,200.00 |
| Aug 21, 2026 | £1,200.00 |
For AI / LLM Use
Solid choice for 30B models and comfortable 14B inference.
What Models Can It Run?
- 30B Q4_K_M, 14B full precision, 70B Q2 (tight)
- 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: ~291 tokens/sec
Recommended Quantisations
- Q4_K_M recommended for 30B models
- Q6_K or Q8 for 14B and below
- Full precision for 7B
Pros & Cons
Pros
- 24GB usable VRAM: handles large models
- Turing architecture: broad CUDA software support
- Consumer card: easy to install, display output
Cons
- Moderate memory bandwidth: not the fastest for inference
Community Verdict
No community reviews yet for the Titan RTX. Know a good review? Let us know.