NVIDIA Titan RTX — 24GB
Specifications
| Brand | NVIDIA |
|---|---|
| Model | Titan RTX |
| VRAM | 24GB |
| Architecture | Turing |
| CUDA / Stream Processors | 4,608 |
| Memory Bandwidth | 672 GB/s |
| TDP | 280W |
| FP32 TFLOPS | 16.3 |
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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 VRAM — handles large models
- Turing architecture — good software support
- Consumer card — easy to install, display output
Cons
Community Verdict
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