NVIDIA RTX 3060 12GB
The NVIDIA RTX 3060 12GB has 12 GB VRAM and 360 GB/s memory bandwidth. It can run 27 of our 80 tracked models natively in VRAM at 8k context.
With 12 GB GDDR6, the NVIDIA RTX 3060 12GB is a consumer-tier GPU that can run 27 models natively. It handles 13B-class models comfortably.
The NVIDIA RTX 3060 12GB is a budget Ampere GPU with a surprisingly generous 12GB GDDR6 on a 192-bit bus at 360 GB/s. The high VRAM-to-price ratio made it a community favorite for local LLM experimentation — running 7B models at Q5–Q8 and 13B models at Q4 with some headroom. Bandwidth is the limiting factor for inference speed.
NVIDIA RTX 3060 12GB: 12GB GDDR6 at 360 GB/s — the budget LLM king.
7B at Q4-Q6 native. 14B at Q4 tight. ~5-8 t/s for 7B.
Full CUDA support. Best sub-$300 GPU for LLM beginners.
| Vendor | NVIDIA |
| Architecture | Ampere |
| VRAM | 12 GB |
| Memory type | GDDR6 |
| Memory bandwidth | 360 GB/s |
| Compute backend | CUDA |
| Tier | Consumer |
| Released | 2021 |
| Models (native) | 27 / 80 |
| Models (offload) | 22 / 80 |
Software: Full llama.cpp and Ollama support out of the box. CUDA 12.x recommended; driver ≥ 525 required.
Popular models for this GPU
Models this GPU runs natively in VRAM (27)
- Bonsai 27B27B · MMLU-Pro 81.5Ternary (Q2_0) · ~26.6 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9Q2_K · ~42.7 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0NVFP4 · ~26.8 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7NVFP4 · ~26.1 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4NVFP4 · ~28.1 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6NVFP4 · ~31.4 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6NVFP4 · ~32.7 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2NVFP4 · ~25.4 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0NVFP4 · ~31.5 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3NVFP4 · ~46.1 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0NVFP4 · ~46.1 t/s
- Qwen3 8B8B · MMLU-Pro 56.7NVFP4 · ~44.9 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3NVFP4 · ~54.8 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0NVFP4 · ~49.8 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6BF16 · ~27.5 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4BF16 · ~26 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4NVFP4 · ~45.7 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0BF16 · ~31.9 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4BF16 · ~36 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8BF16 · ~38.5 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0FP32 · ~27.8 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0FP32 · ~27.8 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8FP32 · ~37.5 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5FP32 · ~44.8 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7FP32 · ~54.1 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0FP32 · ~111.4 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0FP32 · ~131.8 t/s
Models that fit with CPU offload (22)
These use system RAM for layers that don't fit in VRAM — expect much slower inference.
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1Q2_K · ~1.3 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9Q2_K · ~1.3 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0Q2_K · ~1.3 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4Q2_K · ~1.3 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7NVFP4 · ~1.9 t/s
- Command-R 35B35B · MMLU-Pro 33.0NVFP4 · ~1.4 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 84.2NVFP4 · ~9.7 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2NVFP4 · ~2.6 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0NVFP4 · ~2.8 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5NVFP4 · ~3.3 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0NVFP4 · ~3 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 50.4NVFP4 · ~3 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0NVFP4 · ~3 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3NVFP4 · ~12.1 t/s
- Gemma 4 31B31B · MMLU-Pro 85.2NVFP4 · ~2.9 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5NVFP4 · ~12.1 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0NVFP4 · ~3.8 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5NVFP4 · ~5.1 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2NVFP4 · ~5.1 t/s
- Gemma 4 26B (MoE)26B · MMLU-Pro 82.6NVFP4 · ~11 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8NVFP4 · ~7.9 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2NVFP4 · ~8.9 t/s
Too large for this GPU (31)
- Mixtral 8x22B Instruct v0.1
- Llama 3.1 405B Instruct
- DeepSeek V3 671B
- DeepSeek R1 671B
- Llama 4 Scout 109B
- Llama 4 Maverick 400B
- Qwen3 235B-A22B (MoE)
- MiniMax M1 456B
- GPT-OSS 120B
- GLM-4.5 355B
- GLM-4.5 Air 106B
- GLM-4.6 355B
- GLM-4.6V 106B
- GLM-4.7 358B
- Qwen 3.5 122B-A10B (MoE)
- MiniMax M2.5 229B
- GLM-5 744B
- MiniMax M2.7 229B
- Nemotron 3 Super 120B
- Kimi K2.6
- GLM-5.1 754B
- DeepSeek V4 Pro 1.6T
- DeepSeek V4 Flash 284B
- GLM-5.2 753B
- Nemotron 3 Ultra 550B-A55B
- Step 3.5 Flash
- Step 3.7 Flash
- MiMo V2.5 Pro
- Kimi K2.5
- MiniMax M3
- Inkling
Compare NVIDIA RTX 3060 12GB with other GPUs
Continue reading
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Getting Started with Local LLMs (2026): The Complete Beginner's GuideFrequently asked questions
- How much VRAM does the NVIDIA RTX 3060 12GB have?
- The NVIDIA RTX 3060 12GB has 12 GB of GDDR6 with 360 GB/s memory bandwidth.
- What is the NVIDIA RTX 3060 12GB best for?
- With 12 GB of VRAM, the NVIDIA RTX 3060 12GB is best for running compact models (1B–8B) at low quantization, suitable for edge inference, prototyping, and lightweight tasks.
- What LLMs can the NVIDIA RTX 3060 12GB run locally?
- The NVIDIA RTX 3060 12GB can run 27 of the 80 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Llama 3.1 8B Instruct at NVFP4, Llama 3.2 3B Instruct at BF16, Llama 3.2 1B Instruct at FP32.
- Can the NVIDIA RTX 3060 12GB run Llama 3.3 70B Instruct?
- The NVIDIA RTX 3060 12GB can run Llama 3.3 70B Instruct with CPU offload at Q2_K quantization, but inference will be slower than native VRAM execution.
- Can the NVIDIA RTX 3060 12GB run Qwen 3.6 27B?
- The NVIDIA RTX 3060 12GB can run Qwen 3.6 27B with CPU offload at NVFP4 quantization, but inference will be slower than native VRAM execution.
- Can the NVIDIA RTX 3060 12GB run Llama 3.1 8B Instruct?
- Yes. The NVIDIA RTX 3060 12GB runs Llama 3.1 8B Instruct natively in VRAM at NVFP4 quantization, achieving approximately 46.1 tokens per second.