Quick Answer: RTX 4070 Ti offers 12GB VRAM and starts around current market pricing. It delivers approximately 117 tokens/sec on google/gemma-2b. It typically draws 285W under load.
RTX 4070 Ti is the sweet spot for 7B–13B inference. It hits solid tokens/sec without the power demands or price tag of the higher-end Ada cards.
Buy directly on Amazon with fast shipping and reliable customer service.
Rotate out primary variants whenever validation flags an issue.
💡 Not ready to buy? Try cloud GPUs first
Test RTX 4070 Ti performance in the cloud before investing in hardware. Pay by the hour with no commitment.
| Model | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|
| google/gemma-2b | Q4 | 116.83 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 116.79 tok/sEstimated Auto-generated benchmark | 2GB |
| tencent/HunyuanOCR | Q4 | 115.40 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 115.07 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 114.97 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-3-1b-it | Q4 | 114.84 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-research/PowerMoE-3b | Q4 | 114.62 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 113.85 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 112.47 tok/sEstimated Auto-generated benchmark | 2GB |
| google-t5/t5-3b | Q4 | 112.19 tok/sEstimated Auto-generated benchmark | 2GB |
| LiquidAI/LFM2-1.2B | Q4 | 110.03 tok/sEstimated Auto-generated benchmark | 1GB |
| inference-net/Schematron-3B | Q4 | 109.43 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B | Q4 | 109.35 tok/sEstimated Auto-generated benchmark | 1GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 109.20 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 108.05 tok/sEstimated Auto-generated benchmark | 1GB |
| allenai/OLMo-2-0425-1B | Q4 | 107.93 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 107.76 tok/sEstimated Auto-generated benchmark | 1GB |
| google-bert/bert-base-uncased | Q4 | 107.18 tok/sEstimated Auto-generated benchmark | 1GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 105.27 tok/sEstimated Auto-generated benchmark | 2GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 105.01 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 104.49 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B | Q4 | 104.23 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2-2b-it | Q4 | 102.69 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 101.66 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 101.39 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B | Q4 | 100.87 tok/sEstimated Auto-generated benchmark | 2GB |
| bigcode/starcoder2-3b | Q4 | 100.75 tok/sEstimated Auto-generated benchmark | 2GB |
| google/embeddinggemma-300m | Q4 | 99.84 tok/sEstimated Auto-generated benchmark | 1GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 99.78 tok/sEstimated Auto-generated benchmark | 1GB |
| black-forest-labs/FLUX.1-dev | Q4 | 97.06 tok/sEstimated Auto-generated benchmark | 4GB |
| google/gemma-3-270m-it | Q4 | 96.98 tok/sEstimated Auto-generated benchmark | 4GB |
| NousResearch/Meta-Llama-3.1-8B-Instruct | Q4 | 96.93 tok/sEstimated Auto-generated benchmark | 4GB |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-8bit | Q4 | 96.71 tok/sEstimated Auto-generated benchmark | 4GB |
| bigscience/bloomz-560m | Q4 | 96.64 tok/sEstimated Auto-generated benchmark | 4GB |
| GSAI-ML/LLaDA-8B-Instruct | Q4 | 96.57 tok/sEstimated Auto-generated benchmark | 4GB |
| nari-labs/Dia2-2B | Q4 | 96.55 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/gemma-3-1b-it | Q4 | 96.48 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/mistral-7b-v0.3-bnb-4bit | Q4 | 96.46 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Thinking-2507-FP8 | Q4 | 95.98 tok/sEstimated Auto-generated benchmark | 2GB |
| llamafactory/tiny-random-Llama-3 | Q4 | 95.78 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-R1 | Q4 | 95.67 tok/sEstimated Auto-generated benchmark | 4GB |
| HuggingFaceTB/SmolLM-135M | Q4 | 95.64 tok/sEstimated Auto-generated benchmark | 4GB |
| rednote-hilab/dots.ocr | Q4 | 95.54 tok/sEstimated Auto-generated benchmark | 4GB |
| Tongyi-MAI/Z-Image-Turbo | Q4 | 95.42 tok/sEstimated Auto-generated benchmark | 4GB |
| dicta-il/dictalm2.0-instruct | Q4 | 95.33 tok/sEstimated Auto-generated benchmark | 4GB |
| skt/kogpt2-base-v2 | Q4 | 95.29 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-Embedding-0.6B | Q4 | 95.24 tok/sEstimated Auto-generated benchmark | 3GB |
| meta-llama/Meta-Llama-3-8B-Instruct | Q4 | 95.08 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-2-7b-hf | Q4 | 94.95 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-Guard-3-8B | Q4 | 94.61 tok/sEstimated Auto-generated benchmark | 4GB |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
| Model | Quantization | Verdict | Estimated speed | VRAM needed |
|---|---|---|---|---|
| lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-6bit | Q4 | Not supported | 46.67 tok/sEstimated | 15GB (have 12GB) |
| lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-6bit | Q8 | Not supported | 33.40 tok/sEstimated | 31GB (have 12GB) |
| Qwen/Qwen2.5-72B-Instruct | FP16 | Not supported | 6.99 tok/sEstimated | 142GB (have 12GB) |
| Qwen/Qwen2.5-7B-Instruct | Q4 | Fits comfortably | 88.74 tok/sEstimated | 4GB (have 12GB) |
| mistralai/Mistral-Large-Instruct-2411 | Q8 | Not supported | 12.25 tok/sEstimated | 120GB (have 12GB) |
| microsoft/Phi-3.5-mini-instruct | Q8 | Fits comfortably | 56.92 tok/sEstimated | 4GB (have 12GB) |
| microsoft/Phi-3.5-mini-instruct | FP16 | Fits comfortably | 34.46 tok/sEstimated | 8GB (have 12GB) |
| openai/gpt-oss-120b | FP16 | Not supported | 6.95 tok/sEstimated | 235GB (have 12GB) |
| petals-team/StableBeluga2 | FP16 | Not supported | 33.50 tok/sEstimated | 15GB (have 12GB) |
| meta-llama/Llama-3.2-1B | FP16 | Fits comfortably | 39.36 tok/sEstimated | 2GB (have 12GB) |
| meta-llama/Meta-Llama-3-8B | Q4 | Fits comfortably | 83.81 tok/sEstimated | 4GB (have 12GB) |
| Qwen/Qwen3-Next-80B-A3B-Instruct | Q4 | Not supported | 16.35 tok/sEstimated | 39GB (have 12GB) |
| vikhyatk/moondream2 | Q8 | Fits comfortably | 66.41 tok/sEstimated | 7GB (have 12GB) |
| vikhyatk/moondream2 | FP16 | Not supported | 35.49 tok/sEstimated | 15GB (have 12GB) |
| petals-team/StableBeluga2 | Q4 | Fits comfortably | 92.35 tok/sEstimated | 4GB (have 12GB) |
| petals-team/StableBeluga2 | Q8 | Fits comfortably | 57.52 tok/sEstimated | 7GB (have 12GB) |
| meta-llama/Llama-3.2-1B | Q4 | Fits comfortably | 109.35 tok/sEstimated | 1GB (have 12GB) |
| meta-llama/Llama-3.2-1B | Q8 | Fits comfortably | 73.35 tok/sEstimated | 1GB (have 12GB) |
| meta-llama/Meta-Llama-3-8B | Q8 | Fits comfortably | 58.40 tok/sEstimated | 9GB (have 12GB) |
| meta-llama/Meta-Llama-3-8B | FP16 | Not supported | 36.45 tok/sEstimated | 17GB (have 12GB) |
| Qwen/Qwen2.5-7B | Q4 | Fits comfortably | 83.31 tok/sEstimated | 4GB (have 12GB) |
| Qwen/Qwen2.5-0.5B-Instruct | FP16 | Fits (tight) | 33.09 tok/sEstimated | 11GB (have 12GB) |
| Qwen/Qwen3-Next-80B-A3B-Instruct | Q8 | Not supported | 12.25 tok/sEstimated | 78GB (have 12GB) |
| Qwen/Qwen3-Next-80B-A3B-Instruct | FP16 | Not supported | 7.13 tok/sEstimated | 156GB (have 12GB) |
| allenai/OLMo-2-0425-1B | Q4 | Fits comfortably | 107.93 tok/sEstimated | 1GB (have 12GB) |
| microsoft/Phi-3-mini-4k-instruct | Q4 | Fits comfortably | 92.66 tok/sEstimated | 4GB (have 12GB) |
| microsoft/Phi-3-mini-4k-instruct | Q8 | Fits comfortably | 60.96 tok/sEstimated | 7GB (have 12GB) |
| microsoft/Phi-3-mini-4k-instruct | FP16 | Not supported | 32.12 tok/sEstimated | 15GB (have 12GB) |
| openai-community/gpt2-large | Q4 | Fits comfortably | 91.09 tok/sEstimated | 4GB (have 12GB) |
| Qwen/Qwen3-30B-A3B-Instruct-2507 | Q4 | Not supported | 51.15 tok/sEstimated | 15GB (have 12GB) |
| Qwen/Qwen3-30B-A3B-Instruct-2507 | Q8 | Not supported | 33.76 tok/sEstimated | 31GB (have 12GB) |
| Qwen/Qwen3-30B-A3B-Instruct-2507 | FP16 | Not supported | 19.48 tok/sEstimated | 61GB (have 12GB) |
| rednote-hilab/dots.ocr | Q4 | Fits comfortably | 95.54 tok/sEstimated | 4GB (have 12GB) |
| Qwen/Qwen3-Reranker-0.6B | FP16 | Not supported | 36.95 tok/sEstimated | 13GB (have 12GB) |
| meta-llama/Meta-Llama-3-8B-Instruct | Q4 | Fits comfortably | 95.08 tok/sEstimated | 4GB (have 12GB) |
| mlx-community/gpt-oss-20b-MXFP4-Q8 | Q4 | Fits comfortably | 53.19 tok/sEstimated | 10GB (have 12GB) |
| mlx-community/gpt-oss-20b-MXFP4-Q8 | Q8 | Not supported | 35.04 tok/sEstimated | 20GB (have 12GB) |
| mlx-community/gpt-oss-20b-MXFP4-Q8 | FP16 | Not supported | 19.96 tok/sEstimated | 41GB (have 12GB) |
| Qwen/Qwen2.5-1.5B | Q8 | Fits comfortably | 58.89 tok/sEstimated | 5GB (have 12GB) |
| Qwen/Qwen2.5-1.5B | FP16 | Fits (tight) | 31.03 tok/sEstimated | 11GB (have 12GB) |
| Qwen/Qwen2.5-14B-Instruct | FP16 | Not supported | 26.18 tok/sEstimated | 29GB (have 12GB) |
| unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit | Q4 | Fits comfortably | 94.23 tok/sEstimated | 4GB (have 12GB) |
| unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit | Q8 | Fits comfortably | 59.17 tok/sEstimated | 9GB (have 12GB) |
| microsoft/phi-2 | Q4 | Fits comfortably | 93.79 tok/sEstimated | 4GB (have 12GB) |
| microsoft/phi-2 | Q8 | Fits comfortably | 59.46 tok/sEstimated | 7GB (have 12GB) |
| microsoft/phi-2 | FP16 | Not supported | 35.92 tok/sEstimated | 15GB (have 12GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | Q4 | Fits comfortably | 89.53 tok/sEstimated | 4GB (have 12GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | Q8 | Fits comfortably | 60.83 tok/sEstimated | 7GB (have 12GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | FP16 | Not supported | 35.29 tok/sEstimated | 15GB (have 12GB) |
| GSAI-ML/LLaDA-8B-Base | FP16 | Not supported | 31.59 tok/sEstimated | 17GB (have 12GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
Data-backed answers pulled from community benchmarks, manufacturer specs, and live pricing.
A CUDA user benchmarking Qwen 2.5 14B Instruct Q4_K on a 4070 Ti Super reported ~72 tokens/sec—roughly 40% faster than the LocalScore baseline for that quant.
Source: Reddit – /r/LocalLLaMA (mlbgc2j)
LM Studio logs ~35 tok/s on gemma2-9b Q8_0 and ~25 tok/s on gemma-2-27b Q4_K_M with dual 4070 Ti Supers, delivering sub-0.2 s time-to-first-token for 9B workloads.
Source: Reddit – /r/LocalLLaMA (mehsra3)
Users on 12 GB mobile 4070 Ti rigs see QWQ stuck near 3.7 tok/s until they manually offload additional layers to the GPU—highlighting how VRAM ceilings restrict high-context models.
Source: Reddit – /r/LocalLLaMA (mjbgky4)
RTX 4070 Ti is rated at 285 W, ships with 12 GB GDDR6X, and relies on the 16-pin 12VHPWR connector. NVIDIA’s PSU guidance is 700 W or higher.
Source: TechPowerUp – RTX 4070 Ti Specs
As of Nov 2025, Amazon listed the 4070 Ti at $799 in stock.
Source: Supabase price tracker snapshot – 2025-11-03
Explore how RTX 4080 stacks up for local inference workloads.
Explore how RTX 4070 stacks up for local inference workloads.
Explore how RTX 3080 stacks up for local inference workloads.
Explore how RTX 4090 stacks up for local inference workloads.
Explore how RX 7900 XT stacks up for local inference workloads.
Side-by-side VRAM, throughput, efficiency, and pricing benchmarks for both GPUs.
Side-by-side VRAM, throughput, efficiency, and pricing benchmarks for both GPUs.
Side-by-side VRAM, throughput, efficiency, and pricing benchmarks for both GPUs.