Quick Answer: RTX 5070 Ti offers 16GB VRAM and starts around $849.99. It delivers approximately 190 tokens/sec on meta-llama/Llama-3.2-3B-Instruct. It typically draws 300W under load.
This GPU offers reliable throughput for local AI workloads. Pair it with the right model quantization to hit your desired tokens/sec, and monitor prices below to catch the best deal.
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| Model | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 190.00 tok/sEstimated Auto-generated benchmark | 2GB |
| bigcode/starcoder2-3b | Q4 | 188.83 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/gemma-3-1b-it | Q4 | 188.06 tok/sEstimated Auto-generated benchmark | 1GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 183.25 tok/sEstimated Auto-generated benchmark | 1GB |
| google-t5/t5-3b | Q4 | 182.36 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B | Q4 | 182.14 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2b | Q4 | 181.13 tok/sEstimated Auto-generated benchmark | 1GB |
| allenai/OLMo-2-0425-1B | Q4 | 181.01 tok/sEstimated Auto-generated benchmark | 1GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 179.95 tok/sEstimated Auto-generated benchmark | 1GB |
| google/embeddinggemma-300m | Q4 | 179.17 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 177.23 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 176.55 tok/sEstimated Auto-generated benchmark | 1GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 174.52 tok/sEstimated Auto-generated benchmark | 1GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 174.46 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-3-1b-it | Q4 | 173.91 tok/sEstimated Auto-generated benchmark | 1GB |
| nari-labs/Dia2-2B | Q4 | 171.95 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-research/PowerMoE-3b | Q4 | 169.45 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 168.76 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 168.48 tok/sEstimated Auto-generated benchmark | 1GB |
| tencent/HunyuanOCR | Q4 | 167.53 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 167.29 tok/sEstimated Auto-generated benchmark | 1GB |
| inference-net/Schematron-3B | Q4 | 166.33 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 163.33 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B | Q4 | 162.46 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 162.16 tok/sEstimated Auto-generated benchmark | 2GB |
| LiquidAI/LFM2-1.2B | Q4 | 160.64 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B | Q4 | 160.20 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2-2b-it | Q4 | 159.72 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 159.07 tok/sEstimated Auto-generated benchmark | 1GB |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-4bit | Q4 | 158.92 tok/sEstimated Auto-generated benchmark | 4GB |
| Tongyi-MAI/Z-Image-Turbo | Q4 | 158.77 tok/sEstimated Auto-generated benchmark | 4GB |
| unsloth/Meta-Llama-3.1-8B-Instruct | Q4 | 158.10 tok/sEstimated Auto-generated benchmark | 4GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 157.86 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/DeepSeek-V3-0324 | Q4 | 157.79 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-1.7B-Base | Q4 | 157.59 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 157.28 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen3-4B | Q4 | 157.22 tok/sEstimated Auto-generated benchmark | 2GB |
| microsoft/Phi-4-multimodal-instruct | Q4 | 156.71 tok/sEstimated Auto-generated benchmark | 4GB |
| GSAI-ML/LLaDA-8B-Instruct | Q4 | 156.35 tok/sEstimated Auto-generated benchmark | 4GB |
| GSAI-ML/LLaDA-8B-Base | Q4 | 156.33 tok/sEstimated Auto-generated benchmark | 4GB |
| google-bert/bert-base-uncased | Q4 | 156.26 tok/sEstimated Auto-generated benchmark | 1GB |
| lmsys/vicuna-7b-v1.5 | Q4 | 155.99 tok/sEstimated Auto-generated benchmark | 4GB |
| zai-org/GLM-4.6-FP8 | Q4 | 155.97 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct | Q4 | 155.76 tok/sEstimated Auto-generated benchmark | 4GB |
| liuhaotian/llava-v1.5-7b | Q4 | 155.74 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/DialoGPT-medium | Q4 | 155.64 tok/sEstimated Auto-generated benchmark | 4GB |
| black-forest-labs/FLUX.1-dev | Q4 | 155.41 tok/sEstimated Auto-generated benchmark | 4GB |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-8bit | Q4 | 154.84 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Thinking-2507 | Q4 | 154.80 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen3-0.6B | Q4 | 154.73 tok/sEstimated Auto-generated benchmark | 3GB |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
| Model | Quantization | Verdict | Estimated speed | VRAM needed |
|---|---|---|---|---|
| distilbert/distilgpt2 | Q8 | Fits comfortably | 94.31 tok/sEstimated | 7GB (have 16GB) |
| RedHatAI/Llama-3.3-70B-Instruct-FP8-dynamic | Q4 | Not supported | 52.04 tok/sEstimated | 34GB (have 16GB) |
| vikhyatk/moondream2 | FP16 | Fits (tight) | 59.84 tok/sEstimated | 15GB (have 16GB) |
| microsoft/Phi-3-mini-4k-instruct | FP16 | Fits (tight) | 50.55 tok/sEstimated | 15GB (have 16GB) |
| Qwen/Qwen2.5-14B-Instruct | FP16 | Not supported | 37.51 tok/sEstimated | 29GB (have 16GB) |
| meta-llama/Llama-3.1-8B | Q8 | Fits comfortably | 101.71 tok/sEstimated | 9GB (have 16GB) |
| meta-llama/Llama-3.1-8B | FP16 | Not supported | 53.86 tok/sEstimated | 17GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-0528 | Q4 | Fits comfortably | 153.15 tok/sEstimated | 4GB (have 16GB) |
| meta-llama/Meta-Llama-3-70B-Instruct | FP16 | Not supported | 20.42 tok/sEstimated | 137GB (have 16GB) |
| unsloth/gpt-oss-20b-BF16 | Q4 | Fits comfortably | 83.52 tok/sEstimated | 10GB (have 16GB) |
| unsloth/gpt-oss-20b-BF16 | FP16 | Not supported | 27.62 tok/sEstimated | 41GB (have 16GB) |
| Qwen/Qwen3-0.6B-Base | Q4 | Fits comfortably | 150.81 tok/sEstimated | 3GB (have 16GB) |
| rinna/japanese-gpt-neox-small | Q4 | Fits comfortably | 143.66 tok/sEstimated | 4GB (have 16GB) |
| microsoft/Phi-3.5-mini-instruct | FP16 | Fits (tight) | 56.34 tok/sEstimated | 15GB (have 16GB) |
| unsloth/DeepSeek-R1-Distill-Qwen-32B-bnb-4bit | Q4 | Fits (tight) | 47.65 tok/sEstimated | 16GB (have 16GB) |
| unsloth/DeepSeek-R1-Distill-Qwen-32B-bnb-4bit | Q8 | Not supported | 34.90 tok/sEstimated | 33GB (have 16GB) |
| unsloth/DeepSeek-R1-Distill-Qwen-32B-bnb-4bit | FP16 | Not supported | 17.82 tok/sEstimated | 66GB (have 16GB) |
| google/gemma-2-2b-it | Q8 | Fits comfortably | 112.60 tok/sEstimated | 2GB (have 16GB) |
| google/gemma-2-2b-it | FP16 | Fits comfortably | 69.51 tok/sEstimated | 4GB (have 16GB) |
| numind/NuExtract-1.5 | Q8 | Fits comfortably | 103.60 tok/sEstimated | 7GB (have 16GB) |
| RedHatAI/Llama-3.2-90B-Vision-Instruct-FP8-dynamic | Q4 | Not supported | 29.07 tok/sEstimated | 44GB (have 16GB) |
| llamafactory/tiny-random-Llama-3 | Q8 | Fits comfortably | 108.28 tok/sEstimated | 7GB (have 16GB) |
| google/gemma-2b | Q8 | Fits comfortably | 119.82 tok/sEstimated | 2GB (have 16GB) |
| liuhaotian/llava-v1.5-7b | Q8 | Fits comfortably | 92.22 tok/sEstimated | 7GB (have 16GB) |
| liuhaotian/llava-v1.5-7b | FP16 | Fits (tight) | 53.50 tok/sEstimated | 15GB (have 16GB) |
| Qwen/Qwen2.5-72B-Instruct | Q4 | Not supported | 28.62 tok/sEstimated | 35GB (have 16GB) |
| Qwen/Qwen2.5-72B-Instruct | Q8 | Not supported | 20.93 tok/sEstimated | 70GB (have 16GB) |
| Qwen/Qwen2.5-72B-Instruct | FP16 | Not supported | 11.77 tok/sEstimated | 141GB (have 16GB) |
| microsoft/VibeVoice-1.5B | Q4 | Fits comfortably | 152.24 tok/sEstimated | 3GB (have 16GB) |
| microsoft/VibeVoice-1.5B | Q8 | Fits comfortably | 108.45 tok/sEstimated | 5GB (have 16GB) |
| microsoft/VibeVoice-1.5B | FP16 | Fits comfortably | 53.69 tok/sEstimated | 11GB (have 16GB) |
| unsloth/gemma-3-1b-it | FP16 | Fits comfortably | 68.31 tok/sEstimated | 2GB (have 16GB) |
| Qwen/Qwen3-Next-80B-A3B-Thinking | Q4 | Not supported | 30.61 tok/sEstimated | 39GB (have 16GB) |
| Qwen/Qwen3-Next-80B-A3B-Thinking | Q8 | Not supported | 18.77 tok/sEstimated | 78GB (have 16GB) |
| Qwen/Qwen3-Next-80B-A3B-Thinking | FP16 | Not supported | 10.24 tok/sEstimated | 156GB (have 16GB) |
| meta-llama/Llama-2-13b-chat-hf | Q4 | Fits comfortably | 98.67 tok/sEstimated | 7GB (have 16GB) |
| Qwen/Qwen2.5-3B | Q8 | Fits comfortably | 111.78 tok/sEstimated | 3GB (have 16GB) |
| Qwen/Qwen2.5-3B | FP16 | Fits comfortably | 63.80 tok/sEstimated | 6GB (have 16GB) |
| nvidia/NVIDIA-Nemotron-Nano-9B-v2 | Q8 | Fits comfortably | 72.93 tok/sEstimated | 10GB (have 16GB) |
| apple/OpenELM-1_1B-Instruct | Q4 | Fits comfortably | 174.52 tok/sEstimated | 1GB (have 16GB) |
| apple/OpenELM-1_1B-Instruct | Q8 | Fits comfortably | 111.70 tok/sEstimated | 1GB (have 16GB) |
| apple/OpenELM-1_1B-Instruct | FP16 | Fits comfortably | 71.66 tok/sEstimated | 2GB (have 16GB) |
| Alibaba-NLP/gte-Qwen2-1.5B-instruct | FP16 | Fits comfortably | 52.03 tok/sEstimated | 11GB (have 16GB) |
| Qwen/Qwen2-0.5B-Instruct | Q8 | Fits comfortably | 95.93 tok/sEstimated | 5GB (have 16GB) |
| deepseek-ai/DeepSeek-Coder-V2-Instruct-0724 | Q4 | Not supported | 21.68 tok/sEstimated | 115GB (have 16GB) |
| deepseek-ai/DeepSeek-Coder-V2-Instruct-0724 | Q8 | Not supported | 13.83 tok/sEstimated | 231GB (have 16GB) |
| meta-llama/Llama-3.3-70B-Instruct | FP16 | Not supported | 10.07 tok/sEstimated | 138GB (have 16GB) |
| meta-llama/Llama-3.1-70B-Instruct | Q4 | Not supported | 27.87 tok/sEstimated | 34GB (have 16GB) |
| meta-llama/Llama-3.1-70B-Instruct | Q8 | Not supported | 19.66 tok/sEstimated | 69GB (have 16GB) |
| distilbert/distilgpt2 | Q4 | Fits comfortably | 149.71 tok/sEstimated | 4GB (have 16GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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