Quick Answer: RTX 4060 Ti 16GB offers 16GB VRAM and starts around $449.99. It delivers approximately 67 tokens/sec on deepseek-ai/DeepSeek-OCR. It typically draws 165W 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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Rotate out primary variants whenever validation flags an issue.
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| Model | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|
| deepseek-ai/DeepSeek-OCR | Q4 | 67.33 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2-2b-it | Q4 | 66.68 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 66.60 tok/sEstimated Auto-generated benchmark | 2GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 66.21 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-research/PowerMoE-3b | Q4 | 66.09 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 64.56 tok/sEstimated Auto-generated benchmark | 2GB |
| google-bert/bert-base-uncased | Q4 | 64.55 tok/sEstimated Auto-generated benchmark | 1GB |
| nari-labs/Dia2-2B | Q4 | 64.54 tok/sEstimated Auto-generated benchmark | 2GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 64.46 tok/sEstimated Auto-generated benchmark | 1GB |
| allenai/OLMo-2-0425-1B | Q4 | 64.38 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 64.26 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-3-1b-it | Q4 | 64.01 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B | Q4 | 63.65 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 63.65 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 63.62 tok/sEstimated Auto-generated benchmark | 1GB |
| tencent/HunyuanOCR | Q4 | 63.48 tok/sEstimated Auto-generated benchmark | 1GB |
| inference-net/Schematron-3B | Q4 | 62.82 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/gemma-3-1b-it | Q4 | 62.25 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 61.93 tok/sEstimated Auto-generated benchmark | 1GB |
| google/embeddinggemma-300m | Q4 | 61.83 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 61.39 tok/sEstimated Auto-generated benchmark | 2GB |
| google-t5/t5-3b | Q4 | 61.00 tok/sEstimated Auto-generated benchmark | 2GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 60.91 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2b | Q4 | 60.54 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 60.09 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B | Q4 | 58.60 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 58.55 tok/sEstimated Auto-generated benchmark | 2GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 57.11 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Meta-Llama-3-8B | Q4 | 55.45 tok/sEstimated Auto-generated benchmark | 4GB |
| LiquidAI/LFM2-1.2B | Q4 | 55.34 tok/sEstimated Auto-generated benchmark | 1GB |
| microsoft/phi-2 | Q4 | 55.22 tok/sEstimated Auto-generated benchmark | 4GB |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-8bit | Q4 | 55.22 tok/sEstimated Auto-generated benchmark | 4GB |
| EleutherAI/pythia-70m-deduped | Q4 | 55.17 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-R1-0528 | Q4 | 54.99 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen-Image-Edit-2509 | Q4 | 54.98 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-3B | Q4 | 54.97 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-7B-Instruct | Q4 | 54.84 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/VibeVoice-1.5B | Q4 | 54.82 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen3-4B-Thinking-2507 | Q4 | 54.78 tok/sEstimated Auto-generated benchmark | 2GB |
| bigcode/starcoder2-3b | Q4 | 54.75 tok/sEstimated Auto-generated benchmark | 2GB |
| llamafactory/tiny-random-Llama-3 | Q4 | 54.59 tok/sEstimated Auto-generated benchmark | 4GB |
| HuggingFaceTB/SmolLM-135M | Q4 | 54.55 tok/sEstimated Auto-generated benchmark | 4GB |
| hmellor/tiny-random-LlamaForCausalLM | Q4 | 54.51 tok/sEstimated Auto-generated benchmark | 4GB |
| rinna/japanese-gpt-neox-small | Q4 | 54.49 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2-1.5B-Instruct | Q4 | 54.44 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen2.5-7B-Instruct | Q4 | 54.42 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B | Q4 | 54.36 tok/sEstimated Auto-generated benchmark | 2GB |
| IlyaGusev/saiga_llama3_8b | Q4 | 54.26 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-Embedding-4B | Q4 | 54.25 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen3-8B-Base | Q4 | 54.04 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 |
|---|---|---|---|---|
| unsloth/Meta-Llama-3.1-8B-Instruct | Q8 | Fits comfortably | 32.65 tok/sEstimated | 9GB (have 16GB) |
| unsloth/Meta-Llama-3.1-8B-Instruct | FP16 | Not supported | 18.07 tok/sEstimated | 17GB (have 16GB) |
| meta-llama/Meta-Llama-3-70B-Instruct | Q4 | Not supported | 18.06 tok/sEstimated | 34GB (have 16GB) |
| Qwen/Qwen3-8B-FP8 | FP16 | Not supported | 19.67 tok/sEstimated | 17GB (have 16GB) |
| deepseek-ai/DeepSeek-R1 | Q8 | Fits comfortably | 38.43 tok/sEstimated | 7GB (have 16GB) |
| deepseek-ai/DeepSeek-R1 | FP16 | Fits (tight) | 20.49 tok/sEstimated | 15GB (have 16GB) |
| hmellor/tiny-random-LlamaForCausalLM | Q4 | Fits comfortably | 54.51 tok/sEstimated | 4GB (have 16GB) |
| GSAI-ML/LLaDA-8B-Instruct | Q8 | Fits comfortably | 34.69 tok/sEstimated | 9GB (have 16GB) |
| Qwen/Qwen3-4B-Thinking-2507 | Q8 | Fits comfortably | 33.35 tok/sEstimated | 4GB (have 16GB) |
| HuggingFaceH4/zephyr-7b-beta | Q4 | Fits comfortably | 50.77 tok/sEstimated | 4GB (have 16GB) |
| distilbert/distilgpt2 | Q4 | Fits comfortably | 52.07 tok/sEstimated | 4GB (have 16GB) |
| distilbert/distilgpt2 | Q8 | Fits comfortably | 37.15 tok/sEstimated | 7GB (have 16GB) |
| distilbert/distilgpt2 | FP16 | Fits (tight) | 19.24 tok/sEstimated | 15GB (have 16GB) |
| meta-llama/Llama-3.2-3B-Instruct | FP16 | Fits comfortably | 21.78 tok/sEstimated | 6GB (have 16GB) |
| vikhyatk/moondream2 | Q4 | Fits comfortably | 52.74 tok/sEstimated | 4GB (have 16GB) |
| meta-llama/Meta-Llama-3-8B | FP16 | Not supported | 17.57 tok/sEstimated | 17GB (have 16GB) |
| Qwen/Qwen2.5-0.5B-Instruct | FP16 | Fits comfortably | 20.18 tok/sEstimated | 11GB (have 16GB) |
| Qwen/Qwen3-32B | Q4 | Fits (tight) | 19.38 tok/sEstimated | 16GB (have 16GB) |
| Qwen/Qwen3-32B | Q8 | Not supported | 12.18 tok/sEstimated | 33GB (have 16GB) |
| Qwen/Qwen3-32B | FP16 | Not supported | 7.05 tok/sEstimated | 66GB (have 16GB) |
| Qwen/Qwen3-Next-80B-A3B-Instruct | Q4 | Not supported | 10.79 tok/sEstimated | 39GB (have 16GB) |
| Qwen/Qwen3-Next-80B-A3B-Instruct | Q8 | Not supported | 6.94 tok/sEstimated | 78GB (have 16GB) |
| Qwen/Qwen3-Next-80B-A3B-Instruct | FP16 | Not supported | 3.79 tok/sEstimated | 156GB (have 16GB) |
| allenai/OLMo-2-0425-1B | Q4 | Fits comfortably | 64.38 tok/sEstimated | 1GB (have 16GB) |
| Qwen/Qwen3-1.7B | Q8 | Fits comfortably | 33.90 tok/sEstimated | 7GB (have 16GB) |
| Qwen/Qwen3-4B | Q8 | Fits comfortably | 36.63 tok/sEstimated | 4GB (have 16GB) |
| Qwen/Qwen3-4B | FP16 | Fits comfortably | 21.01 tok/sEstimated | 9GB (have 16GB) |
| Qwen/Qwen3-30B-A3B-Instruct-2507 | Q4 | Fits (tight) | 30.36 tok/sEstimated | 15GB (have 16GB) |
| Qwen/Qwen3-30B-A3B-Instruct-2507 | Q8 | Not supported | 19.62 tok/sEstimated | 31GB (have 16GB) |
| Qwen/Qwen3-30B-A3B-Instruct-2507 | FP16 | Not supported | 11.38 tok/sEstimated | 61GB (have 16GB) |
| Qwen/Qwen3-Reranker-0.6B | Q8 | Fits comfortably | 34.98 tok/sEstimated | 6GB (have 16GB) |
| Qwen/Qwen3-Reranker-0.6B | FP16 | Fits comfortably | 20.85 tok/sEstimated | 13GB (have 16GB) |
| meta-llama/Meta-Llama-3-8B-Instruct | Q4 | Fits comfortably | 46.28 tok/sEstimated | 4GB (have 16GB) |
| meta-llama/Meta-Llama-3-8B-Instruct | Q8 | Fits comfortably | 38.10 tok/sEstimated | 9GB (have 16GB) |
| meta-llama/Meta-Llama-3-8B-Instruct | FP16 | Not supported | 19.94 tok/sEstimated | 17GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B | Q4 | Fits comfortably | 45.81 tok/sEstimated | 3GB (have 16GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B | Q8 | Fits comfortably | 37.85 tok/sEstimated | 5GB (have 16GB) |
| mlx-community/gpt-oss-20b-MXFP4-Q8 | Q8 | Not supported | 17.65 tok/sEstimated | 20GB (have 16GB) |
| mlx-community/gpt-oss-20b-MXFP4-Q8 | FP16 | Not supported | 9.59 tok/sEstimated | 41GB (have 16GB) |
| Qwen/Qwen2.5-1.5B | Q8 | Fits comfortably | 32.01 tok/sEstimated | 5GB (have 16GB) |
| Qwen/Qwen2.5-14B-Instruct | FP16 | Not supported | 15.28 tok/sEstimated | 29GB (have 16GB) |
| unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit | Q4 | Fits comfortably | 52.12 tok/sEstimated | 4GB (have 16GB) |
| unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit | Q8 | Fits comfortably | 33.71 tok/sEstimated | 9GB (have 16GB) |
| unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit | FP16 | Not supported | 19.78 tok/sEstimated | 17GB (have 16GB) |
| Qwen/Qwen3-Embedding-8B | Q8 | Fits comfortably | 34.77 tok/sEstimated | 9GB (have 16GB) |
| Qwen/Qwen3-Embedding-8B | FP16 | Not supported | 17.49 tok/sEstimated | 17GB (have 16GB) |
| Qwen/Qwen2.5-0.5B | Q8 | Fits comfortably | 32.96 tok/sEstimated | 5GB (have 16GB) |
| Qwen/Qwen2.5-0.5B | FP16 | Fits comfortably | 17.35 tok/sEstimated | 11GB (have 16GB) |
| meta-llama/Llama-3.1-70B-Instruct | Q4 | Not supported | 19.28 tok/sEstimated | 34GB (have 16GB) |
| Qwen/Qwen3-30B-A3B | Q4 | Fits (tight) | 25.26 tok/sEstimated | 15GB (have 16GB) |
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 user benchmarking Llama 2 13B Q4 in Ollama and Open WebUI logged roughly 52 tokens/sec on the 16GB RTX 4060 Ti—about half the speed of a 3090 but smooth enough for interactive use.
Source: Reddit – /r/LocalLLaMA (kt7t5xj)
Yes—with partial CPU offload. Builders report that a 16GB 4060 Ti can run 30B models at 4 bpw, provided enough system RAM is available, whereas the 8GB card quickly stalls.
Source: Reddit – /r/LocalLLaMA (kjyvc7a)
Make sure the card runs on a PCIe 4.0 slot—owners note that the 8-lane interface becomes a major limiter on older PCIe 3.0 boards, slashing throughput during large context runs.
Source: Reddit – /r/LocalLLaMA (kt8pk13)
The 16GB RTX 4060 Ti carries a 165 W board power rating, uses a single 8-pin PCIe connector, and NVIDIA recommends pairing it with a 550 W PSU.
As of Nov 2025 our tracker showed the RTX 4060 Ti 16GB at $499 on Amazon, in stock.
Source: Supabase price tracker snapshot – 2025-11-03
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