NVIDIA GeForce RTX 5070 Ti
NVIDIA · 16GBGDDR7 · Can run 81 models
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| Manufacturer | NVIDIA |
| VRAM | 16 GB |
| Memory Type | GDDR7 |
| Architecture | Blackwell |
| CUDA Cores | 8,960 |
| Tensor Cores | 280 |
| Bandwidth | 896 GB/s |
| TDP | 300W |
| MSRP | $749 |
| Released | Mar 10, 2025 |
AI Notes
The RTX 5070 Ti offers an impressive 896 GB/s bandwidth with 16GB GDDR7 VRAM. It runs 13B models at very fast speeds and can handle 30B models with quantization. The combination of high bandwidth and 16GB capacity makes it one of the best cards for local AI at its price point.
Compatible Models
| Model | Parameters | Best Quant | VRAM Used | Fit | Est. Speed |
|---|---|---|---|---|---|
| Qwen 3 0.6B | 600M | Q4_K_M | 2.5 GB | Runs | ~358 tok/s |
| Qwen 3.5 0.8B | 800M | Q4_K_M | 1.5 GB | Runs | ~597 tok/s |
| Gemma 3 1B | 1B | Q8_0 | 2 GB | Runs | ~448 tok/s |
| Llama 3.2 1B | 1B | Q8_0 | 3 GB | Runs | ~299 tok/s |
| DeepSeek R1 1.5B | 1.5B | Q8_0 | 3 GB | Runs | ~299 tok/s |
| SmolLM2 1.7B | 1.7B | Q8_0 | 2.7 GB | Runs | ~332 tok/s |
| Gemma 2 2B | 2B | Q8_0 | 4 GB | Runs | ~224 tok/s |
| Gemma 3n E2B | 2B | Q4_K_M | 3.3 GB | Runs | ~272 tok/s |
| Gemma 4 E2B | 2B | Q4_K_M | 4 GB | Runs | ~224 tok/s |
| Qwen 3.5 2B | 2B | Q4_K_M | 3 GB | Runs | ~299 tok/s |
| Llama 3.2 3B | 3B | Q8_0 | 5 GB | Runs | ~179 tok/s |
| StarCoder2 3B | 3B | Q4_K_M | 3.5 GB | Runs | ~256 tok/s |
| Phi-3 Mini 3.8B | 3.8B | Q8_0 | 5.8 GB | Runs | ~154 tok/s |
| Phi-4 Mini 3.8B | 3.8B | Q4_K_M | 4.5 GB | Runs | ~199 tok/s |
| Gemma 3 4B | 4B | Q4_K_M | 5 GB | Runs | ~179 tok/s |
| Gemma 3n E4B | 4B | Q4_K_M | 4.5 GB | Runs | ~199 tok/s |
| Gemma 4 E4B | 4B | Q4_K_M | 6 GB | Runs | ~149 tok/s |
| Qwen 3 4B | 4B | Q4_K_M | 4.5 GB | Runs | ~199 tok/s |
| Qwen 3.5 4B | 4B | Q4_K_M | 4.5 GB | Runs | ~199 tok/s |
| Yi 1.5 6B | 6B | Q4_K_M | 5 GB | Runs | ~179 tok/s |
| Codestral Mamba 7B | 7B | Q4_K_M | 6.9 GB | Runs | ~130 tok/s |
| DeepSeek R1 7B | 7B | Q8_0 | 9 GB | Runs | ~100 tok/s |
| Falcon 3 7B | 7B | Q4_K_M | 6.8 GB | Runs | ~132 tok/s |
| InternLM 2.5 7B | 7B | Q4_K_M | 5.5 GB | Runs | ~163 tok/s |
| Mistral 7B | 7B | Q8_0 | 9 GB | Runs | ~100 tok/s |
| OpenChat 3.5 7B | 7B | Q4_K_M | 6.9 GB | Runs | ~130 tok/s |
| Qwen 2.5 7B | 7B | Q8_0 | 9 GB | Runs | ~100 tok/s |
| Qwen 2.5 Coder 7B | 7B | Q8_0 | 9 GB | Runs | ~100 tok/s |
| Qwen 2.5 VL 7B | 7B | Q4_K_M | 7 GB | Runs | ~128 tok/s |
| StarCoder2 7B | 7B | Q4_K_M | 5.5 GB | Runs | ~163 tok/s |
| WizardLM 2 7B | 7B | Q4_K_M | 6.9 GB | Runs | ~130 tok/s |
| Aya Expanse 8B | 8B | Q4_K_M | 6.5 GB | Runs | ~138 tok/s |
| Cogito 8B | 8B | Q4_K_M | 7.5 GB | Runs | ~119 tok/s |
| DeepSeek R1 8B | 8B | Q4_K_M | 7.5 GB | Runs | ~119 tok/s |
| Dolphin 3 8B | 8B | Q4_K_M | 6 GB | Runs | ~149 tok/s |
| Granite 3.3 8B | 8B | Q8_0 | 10 GB | Runs | ~90 tok/s |
| Llama 3.1 8B | 8B | Q8_0 | 10 GB | Runs | ~90 tok/s |
| Nemotron 3 Nano 8B | 8B | Q4_K_M | 7.5 GB | Runs | ~119 tok/s |
| Nous Hermes 2 8B | 8B | Q4_K_M | 6 GB | Runs | ~149 tok/s |
| Qwen 3 8B | 8B | Q4_K_M | 7.5 GB | Runs | ~119 tok/s |
| Gemma 2 9B | 9B | Q8_0 | 11 GB | Runs | ~81 tok/s |
| Qwen 3.5 9B | 9B | Q4_K_M | 7.5 GB | Runs | ~119 tok/s |
| Yi 1.5 9B | 9B | Q4_K_M | 6.5 GB | Runs | ~138 tok/s |
| Yi Coder 9B | 9B | Q4_K_M | 8 GB | Runs | ~112 tok/s |
| Falcon 3 10B | 10B | Q4_K_M | 8.5 GB | Runs | ~105 tok/s |
| Llama 3.2 Vision 11B | 11B | Q4_K_M | 8.5 GB | Runs | ~105 tok/s |
| Gemma 3 12B | 12B | Q4_K_M | 10.5 GB | Runs | ~85 tok/s |
| Mistral Nemo 12B | 12B | Q4_K_M | 9.5 GB | Runs | ~94 tok/s |
| DeepSeek R1 14B | 14B | Q4_K_M | 9.9 GB | Runs | ~91 tok/s |
| Phi-4 14B | 14B | Q4_K_M | 9.9 GB | Runs | ~91 tok/s |
| Phi-4 Reasoning 14B | 14B | Q4_K_M | 11 GB | Runs | ~81 tok/s |
| Qwen 2.5 14B | 14B | Q4_K_M | 9.9 GB | Runs | ~91 tok/s |
| Qwen 2.5 Coder 14B | 14B | Q4_K_M | 12 GB | Runs | ~75 tok/s |
| Qwen 3 14B | 14B | Q4_K_M | 12 GB | Runs | ~75 tok/s |
| InternLM 2.5 20B | 20B | Q4_K_M | 12 GB | Runs | ~75 tok/s |
| Qwen 3.5 35B A3B | 35B | Q4_K_M | 12 GB | Runs | ~75 tok/s |
| gpt-oss 20B | 21B | MXFP4 | 15 GB | Runs (tight) | ~60 tok/s |
| Codestral 22B | 22B | Q4_K_M | 14.7 GB | Runs (tight) | ~61 tok/s |
| StarCoder2 15B | 15B | Q8_0 | 17 GB | CPU Offload | ~16 tok/s |
| Devstral 24B | 24B | Q4_K_M | 17 GB | CPU Offload | ~16 tok/s |
| Magistral Small 24B | 24B | Q4_K_M | 17 GB | CPU Offload | ~16 tok/s |
| Mistral Small 3.1 24B | 24B | Q4_K_M | 18 GB | CPU Offload | ~15 tok/s |
| Gemma 4 26B | 26B | Q4_K_M | 20 GB | CPU Offload | ~14 tok/s |
| Gemma 2 27B | 27B | Q4_K_M | 17.7 GB | CPU Offload | ~15 tok/s |
| Gemma 3 27B | 27B | Q4_K_M | 20 GB | CPU Offload | ~14 tok/s |
| Qwen 3.5 27B | 27B | Q4_K_M | 19 GB | CPU Offload | ~14 tok/s |
| Qwen 3.6 27B | 27B | Q4_K_M | 20 GB | CPU Offload | ~14 tok/s |
| Qwen 3 30B-A3B (MoE) | 30B | Q4_K_M | 22 GB | CPU Offload | ~12 tok/s |
| Gemma 4 31B | 31B | Q4_K_M | 22 GB | CPU Offload | ~12 tok/s |
| Aya Expanse 32B | 32B | Q4_K_M | 22 GB | CPU Offload | ~12 tok/s |
| Cogito 32B | 32B | Q4_K_M | 21.5 GB | CPU Offload | ~13 tok/s |
| DeepSeek R1 32B | 32B | Q4_K_M | 20.7 GB | CPU Offload | ~13 tok/s |
| Qwen 2.5 32B | 32B | Q4_K_M | 20.7 GB | CPU Offload | ~13 tok/s |
| Qwen 2.5 Coder 32B | 32B | Q4_K_M | 23 GB | CPU Offload | ~12 tok/s |
| Qwen 3 32B | 32B | Q4_K_M | 23 GB | CPU Offload | ~12 tok/s |
| QwQ 32B | 32B | Q4_K_M | 21.5 GB | CPU Offload | ~13 tok/s |
| Laguna XS 2.1 | 33B | Q4_K_M | 22 GB | CPU Offload | ~12 tok/s |
| WizardCoder 33B | 33B | Q4_K_M | 22 GB | CPU Offload | ~12 tok/s |
| Nous Hermes 2 34B | 34B | Q4_K_M | 19 GB | CPU Offload | ~14 tok/s |
| Yi 1.5 34B | 34B | Q4_K_M | 21 GB | CPU Offload | ~13 tok/s |
| Command R 35B | 35B | Q4_K_M | 22.5 GB | CPU Offload | ~12 tok/s |
33 model(s) are too large for this hardware.