Quick Answer: RTX 3060 12GB offers 12GB VRAM and starts around $309.99. It delivers approximately 77 tokens/sec on meta-llama/Llama-3.2-3B-Instruct. It typically draws 170W 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 |
|---|---|---|---|
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 76.52 tok/sEstimated Auto-generated benchmark | 2GB |
| allenai/OLMo-2-0425-1B | Q4 | 76.14 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 75.86 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 75.25 tok/sEstimated Auto-generated benchmark | 1GB |
| nari-labs/Dia2-2B | Q4 | 74.31 tok/sEstimated Auto-generated benchmark | 2GB |
| inference-net/Schematron-3B | Q4 | 73.69 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 72.99 tok/sEstimated Auto-generated benchmark | 1GB |
| bigcode/starcoder2-3b | Q4 | 72.15 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 71.78 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 71.38 tok/sEstimated Auto-generated benchmark | 1GB |
| LiquidAI/LFM2-1.2B | Q4 | 70.47 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-research/PowerMoE-3b | Q4 | 70.21 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B | Q4 | 70.18 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 70.15 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-3-1b-it | Q4 | 69.46 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 69.22 tok/sEstimated Auto-generated benchmark | 2GB |
| google/embeddinggemma-300m | Q4 | 69.13 tok/sEstimated Auto-generated benchmark | 1GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 68.66 tok/sEstimated Auto-generated benchmark | 1GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 67.97 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2-2b-it | Q4 | 67.82 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 67.78 tok/sEstimated Auto-generated benchmark | 2GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 67.65 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 67.25 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/gemma-3-1b-it | Q4 | 66.77 tok/sEstimated Auto-generated benchmark | 1GB |
| google-t5/t5-3b | Q4 | 66.74 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2b | Q4 | 66.22 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B | Q4 | 65.62 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B | Q4 | 65.56 tok/sEstimated Auto-generated benchmark | 2GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 65.12 tok/sEstimated Auto-generated benchmark | 2GB |
| google-bert/bert-base-uncased | Q4 | 64.48 tok/sEstimated Auto-generated benchmark | 1GB |
| zai-org/GLM-4.6-FP8 | Q4 | 63.86 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/Phi-4-multimodal-instruct | Q4 | 63.76 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/Phi-3-mini-128k-instruct | Q4 | 63.75 tok/sEstimated Auto-generated benchmark | 4GB |
| IlyaGusev/saiga_llama3_8b | Q4 | 63.64 tok/sEstimated Auto-generated benchmark | 4GB |
| distilbert/distilgpt2 | Q4 | 63.52 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-R1-0528 | Q4 | 63.49 tok/sEstimated Auto-generated benchmark | 4GB |
| unsloth/Meta-Llama-3.1-8B-Instruct | Q4 | 63.19 tok/sEstimated Auto-generated benchmark | 4GB |
| openai-community/gpt2 | Q4 | 63.10 tok/sEstimated Auto-generated benchmark | 4GB |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-6bit | Q4 | 63.09 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Meta-Llama-3-8B-Instruct | Q4 | 62.94 tok/sEstimated Auto-generated benchmark | 4GB |
| HuggingFaceTB/SmolLM2-135M | Q4 | 62.83 tok/sEstimated Auto-generated benchmark | 4GB |
| tencent/HunyuanOCR | Q4 | 62.71 tok/sEstimated Auto-generated benchmark | 1GB |
| petals-team/StableBeluga2 | Q4 | 62.27 tok/sEstimated Auto-generated benchmark | 4GB |
| vikhyatk/moondream2 | Q4 | 62.15 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-Math-1.5B | Q4 | 62.02 tok/sEstimated Auto-generated benchmark | 3GB |
| microsoft/Phi-3-mini-4k-instruct | Q4 | 61.89 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-3.1-8B | Q4 | 61.69 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-R1 | Q4 | 61.67 tok/sEstimated Auto-generated benchmark | 4GB |
| ibm-granite/granite-docling-258M | Q4 | 61.55 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-0.5B-Instruct | Q4 | 61.42 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 |
|---|---|---|---|---|
| black-forest-labs/FLUX.1-dev | FP16 | Not supported | 22.16 tok/sEstimated | 16GB (have 12GB) |
| openai-community/gpt2 | FP16 | Not supported | 20.76 tok/sEstimated | 15GB (have 12GB) |
| meta-llama/Llama-3.1-8B-Instruct | Q4 | Fits comfortably | 54.37 tok/sEstimated | 4GB (have 12GB) |
| meta-llama/Llama-3.1-8B-Instruct | Q8 | Fits comfortably | 44.03 tok/sEstimated | 9GB (have 12GB) |
| openai/gpt-oss-20b | Q8 | Not supported | 21.29 tok/sEstimated | 20GB (have 12GB) |
| openai/gpt-oss-20b | FP16 | Not supported | 11.28 tok/sEstimated | 41GB (have 12GB) |
| google/gemma-3-1b-it | Q4 | Fits comfortably | 69.46 tok/sEstimated | 1GB (have 12GB) |
| google/gemma-3-1b-it | Q8 | Fits comfortably | 51.78 tok/sEstimated | 1GB (have 12GB) |
| facebook/opt-125m | Q4 | Fits comfortably | 53.19 tok/sEstimated | 4GB (have 12GB) |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q8 | Fits comfortably | 51.19 tok/sEstimated | 1GB (have 12GB) |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | FP16 | Fits comfortably | 26.14 tok/sEstimated | 2GB (have 12GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q8 | Fits comfortably | 36.94 tok/sEstimated | 7GB (have 12GB) |
| Qwen/Qwen3-4B-Instruct-2507 | Q8 | Fits comfortably | 38.10 tok/sEstimated | 4GB (have 12GB) |
| Qwen/Qwen3-4B-Instruct-2507 | FP16 | Fits comfortably | 20.40 tok/sEstimated | 9GB (have 12GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | Fits comfortably | 72.99 tok/sEstimated | 1GB (have 12GB) |
| openai/gpt-oss-120b | Q8 | Not supported | 8.27 tok/sEstimated | 117GB (have 12GB) |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q8 | Fits comfortably | 49.39 tok/sEstimated | 3GB (have 12GB) |
| mistralai/Mistral-7B-Instruct-v0.2 | FP16 | Not supported | 23.44 tok/sEstimated | 15GB (have 12GB) |
| Qwen/Qwen3-8B | Q4 | Fits comfortably | 60.40 tok/sEstimated | 4GB (have 12GB) |
| inference-net/Schematron-3B | Q8 | Fits comfortably | 48.91 tok/sEstimated | 3GB (have 12GB) |
| inference-net/Schematron-3B | FP16 | Fits comfortably | 24.51 tok/sEstimated | 6GB (have 12GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-32B | Q4 | Not supported | 19.95 tok/sEstimated | 16GB (have 12GB) |
| meta-llama/Llama-3.2-1B | Q4 | Fits comfortably | 70.18 tok/sEstimated | 1GB (have 12GB) |
| meta-llama/Llama-3.2-1B | Q8 | Fits comfortably | 50.98 tok/sEstimated | 1GB (have 12GB) |
| meta-llama/Meta-Llama-3-8B | Q8 | Fits comfortably | 37.85 tok/sEstimated | 9GB (have 12GB) |
| meta-llama/Meta-Llama-3-8B | FP16 | Not supported | 23.96 tok/sEstimated | 17GB (have 12GB) |
| Qwen/Qwen2.5-7B | Q4 | Fits comfortably | 58.49 tok/sEstimated | 4GB (have 12GB) |
| Qwen/Qwen2.5-7B | Q8 | Fits comfortably | 39.96 tok/sEstimated | 7GB (have 12GB) |
| allenai/OLMo-2-0425-1B | Q4 | Fits comfortably | 76.14 tok/sEstimated | 1GB (have 12GB) |
| allenai/OLMo-2-0425-1B | Q8 | Fits comfortably | 46.62 tok/sEstimated | 1GB (have 12GB) |
| openai-community/gpt2-large | Q8 | Fits comfortably | 44.01 tok/sEstimated | 7GB (have 12GB) |
| openai-community/gpt2-large | FP16 | Not supported | 23.83 tok/sEstimated | 15GB (have 12GB) |
| Qwen/Qwen3-1.7B | Q4 | Fits comfortably | 53.15 tok/sEstimated | 4GB (have 12GB) |
| Qwen/Qwen3-Reranker-0.6B | Q4 | Fits comfortably | 54.84 tok/sEstimated | 3GB (have 12GB) |
| Qwen/Qwen3-Reranker-0.6B | Q8 | Fits comfortably | 39.01 tok/sEstimated | 6GB (have 12GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | Q4 | Not supported | 21.90 tok/sEstimated | 17GB (have 12GB) |
| Qwen/Qwen3-Reranker-0.6B | FP16 | Not supported | 20.14 tok/sEstimated | 13GB (have 12GB) |
| meta-llama/Meta-Llama-3-8B-Instruct | Q8 | Fits comfortably | 36.94 tok/sEstimated | 9GB (have 12GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | Q8 | Not supported | 14.85 tok/sEstimated | 35GB (have 12GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | FP16 | Not supported | 7.17 tok/sEstimated | 70GB (have 12GB) |
| openai/gpt-oss-20b | Q4 | Fits comfortably | 30.47 tok/sEstimated | 10GB (have 12GB) |
| kaitchup/Phi-3-mini-4k-instruct-gptq-4bit | Q8 | Fits comfortably | 43.82 tok/sEstimated | 4GB (have 12GB) |
| kaitchup/Phi-3-mini-4k-instruct-gptq-4bit | FP16 | Fits comfortably | 19.98 tok/sEstimated | 9GB (have 12GB) |
| Qwen/Qwen2.5-1.5B | Q4 | Fits comfortably | 53.79 tok/sEstimated | 3GB (have 12GB) |
| Qwen/Qwen2.5-1.5B | FP16 | Fits (tight) | 23.42 tok/sEstimated | 11GB (have 12GB) |
| unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit | Q8 | Fits comfortably | 38.75 tok/sEstimated | 9GB (have 12GB) |
| Qwen/Qwen3-Embedding-8B | Q4 | Fits comfortably | 55.70 tok/sEstimated | 4GB (have 12GB) |
| Qwen/Qwen3-14B | FP16 | Not supported | 16.36 tok/sEstimated | 29GB (have 12GB) |
| Qwen/Qwen2.5-0.5B | Q4 | Fits comfortably | 59.91 tok/sEstimated | 3GB (have 12GB) |
| openai/gpt-oss-120b | FP16 | Not supported | 4.67 tok/sEstimated | 235GB (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.
Even with modest specs, a 12 GB RTX 3060 can drive 7B Q8 quant models at over 60 tokens/sec—fast enough for iterative coding and agents.
Source: Reddit – /r/LocalLLaMA (l6nfptd)
One builder running three RTX 3060 cards reports Gemma 3 27B Q4 at ~15 tok/sec, Mistral 24B Q4 at ~18 tok/sec, and DeepSeek R1 32B Q4 at ~20 tok/sec via Ollama.
Source: Reddit – /r/LocalLLaMA (mo6ttds)
Not always—2× RTX 3060 was projected to hit ~29 tok/sec on DeepSeek R1 32B (16K ctx), but real benchmarks landed closer to 14 tok/sec.
Source: Reddit – /r/LocalLLaMA (mq781cj)
A dual-Xeon workstation without GPU offload only mustered ~1.68 tok/sec on DeepSeek R1 Q4—showing why even a single 3060 is a major upgrade.
Source: Reddit – /r/LocalLLaMA (mm9ladj)
RTX 3060 12 GB draws 170 W, uses an 8-pin PCIe connector, and NVIDIA recommends a 550 W PSU. As of Nov 2025 the card was around $329 on Amazon.
Source: TechPowerUp – RTX 3060 Specs
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