Best Local AI Models for NVIDIA RTX 3060 12 GB (12 GB VRAM)
The NVIDIA RTX 3060 12 GB has 12 GB of VRAM. Here are the popular AI models it can run locally (4,096-token context, ~16.0 GB system RAM assumed), ranked by popularity.
See also: Best GPU for running local LLMs.
The NVIDIA RTX 3060 12 GB is built on NVIDIA's Ampere architecture featuring GDDR6 192-bit delivering 360 GB/s of raw memory bandwidth. The undisputed budget champion of local AI: 12 GB VRAM on a sub-$300 GPU enables native 14B inference.
Equipped with 12 GB of dedicated VRAM, the NVIDIA RTX 3060 12 GB can run 33 popular open-source models completely in GPU memory without offloading. This includes full-speed execution for weights like Qwen3-8B-GGUF, Qwen3-4B-GGUF, Qwen2.5-7B-Instruct-GGUF. For a comprehensive breakdown of compatible model weights, see our guide to the best LLMs for 12 GB VRAM.
With a memory bandwidth of 360 GB/s, this card can generate tokens at an estimated peak rate of ~60.0 tokens/second on an 8B parameter model (Q4_K_M). Its rated power draw is 170W TDP, so ensure your system's power supply and case ventilation are adequate for sustained local inferencing.
How the NVIDIA RTX 3060 12 GB compares with similar cards
- Compared with the NVIDIA RTX 4070, the NVIDIA RTX 3060 12 GB has the same 12 GB of VRAM, so the same models fit fully on the GPU; about 29% slower token generation (~60 vs ~84 tokens/s on an 8B model in Q4, from 360 vs 504 GB/s); a lower board power (170 vs 200 W) and an older architecture (Ampere vs Ada Lovelace). Side-by-side comparison
- Compared with the NVIDIA RTX 4070 Super, the NVIDIA RTX 3060 12 GB has the same 12 GB of VRAM, so the same models fit fully on the GPU; about 29% slower token generation (~60 vs ~84 tokens/s on an 8B model in Q4, from 360 vs 504 GB/s); a lower board power (170 vs 220 W) and an older architecture (Ampere vs Ada Lovelace). Side-by-side comparison
- Compared with the NVIDIA RTX 4070 Ti, the NVIDIA RTX 3060 12 GB has the same 12 GB of VRAM, so the same models fit fully on the GPU; about 29% slower token generation (~60 vs ~84 tokens/s on an 8B model in Q4, from 360 vs 504 GB/s); a lower board power (170 vs 285 W) and an older architecture (Ampere vs Ada Lovelace). Side-by-side comparison
Token speeds here are a conservative estimate (memory bandwidth ÷ 6, for an 8B model in Q4 that reads about 5 GB of weights per token, plus overhead); prompt processing depends on GPU compute instead.
33 fit fully in VRAM · 3 run with offload
| Model | Size | Quant. | Quality | Memory | Speed~ | Verdict |
|---|---|---|---|---|---|---|
| Qwen/Qwen3-8B-GGUF | 8.19B | Q8_0 | Excellent |
9.47 GB
|
44.4 t/s | Fits in VRAM |
| Qwen/Qwen3-4B-GGUF | 4.02B | Q8_0 | Excellent |
5.35 GB
|
90.3 t/s | Fits in VRAM |
| Qwen/Qwen2.5-7B-Instruct-GGUF | 7.62B | Q8_0 | Excellent |
8.56 GB
|
47.7 t/s | Fits in VRAM |
| unsloth/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF | 8.0B | Q8_K_XL | Excellent |
8.48 GB
|
47.2 t/s | Fits in VRAM |
| unsloth/gpt-oss-20b-GGUF | 20.91B | Q4_K_XL | Good |
11.95 GB
|
32.6 t/s | Fits in VRAM |
| bartowski/Qwen2.5-32B-Instruct-GGUF | 32.76B | IQ2_S | Very low |
11.47 GB
|
37.2 t/s | Fits in VRAM |
| Qwen/Qwen2.5-1.5B-Instruct-GGUF | 1.54B | GGUF | Excellent |
4.23 GB
|
108.6 t/s | Fits in VRAM |
| unsloth/LFM2.5-24B-A2B-GGUF | 23.97B | Q3_K_XL | Fair |
11.07 GB
|
35.3 t/s | Fits in VRAM |
| unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF | 30.53B | Q2_K_L | Low |
11.73 GB
|
34.1 t/s | Fits in VRAM |
| Qwen/Qwen2.5-0.5B-Instruct-GGUF | 0.49B | GGUF | Excellent |
2.03 GB
|
305.2 t/s | Fits in VRAM |
| unsloth/Ornith-1.0-9B-GGUF | — | Q8_0 | Excellent |
9.8 GB
|
40.6 t/s | Fits in VRAM |
| Qwen/Qwen3-14B-GGUF | 14.77B | Q5_K_M | Very good |
11.22 GB
|
36.8 t/s | Fits in VRAM |
| Qwen/Qwen2.5-Coder-7B-Instruct-GGUF | 7.62B | Q8_0 | Excellent |
8.56 GB
|
47.7 t/s | Fits in VRAM |
| ggml-org/gpt-oss-120b-GGUF | 116.83B | BF16 | Very low |
2.42 GB
|
243.6 t/s | Fits in VRAM |
| LiquidAI/LFM3-1.7B-GGUF | 1.73B | BF16 | Excellent |
4.08 GB
|
111.5 t/s | Fits in VRAM |
| unsloth/flint-GGUF | 39.79B | IQ2_XXS | Very low |
11.95 GB
|
32.6 t/s | Fits in VRAM |
| janhq/Jan-v3.5-4B-gguf | 4.41B | GGUF | Excellent |
9.59 GB
|
43.8 t/s | Fits in VRAM |
| Qwen/Qwen2.5-3B-Instruct-GGUF | 3.09B | GGUF | Excellent |
7.27 GB
|
56.8 t/s | Fits in VRAM |
| unsloth/GLM-4.7-Flash-GGUF | 31.22B | Q2_K_L | Low |
11.64 GB
|
33.9 t/s | Fits in VRAM |
| bartowski/Meta-Llama-3.1-8B-Instruct-GGUF | 8.03B | Q8_0 | Excellent |
9.23 GB
|
45.3 t/s | Fits in VRAM |
| unsloth/Llama-3.2-3B-Instruct-GGUF | 3.21B | F16 | Excellent |
7.23 GB
|
60.1 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-1.2B-Instruct-GGUF | 1.17B | BF16 | Excellent |
3.03 GB
|
165.0 t/s | Fits in VRAM |
| lmstudio-community/gemma-3-1b-it-GGUF | 1.0B | Q8_0 | Excellent |
1.96 GB
|
361.5 t/s | Fits in VRAM |
| bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF | 34.66B | IQ2_S | Very low |
11.13 GB
|
35.1 t/s | Fits in VRAM |
| Qwen/Qwen3-0.6B-GGUF | 0.75B | Q8_0 | Excellent |
1.83 GB
|
604.5 t/s | Fits in VRAM |
| bartowski/Qwen_Qwen3-0.6B-GGUF | 0.75B | BF16 | Excellent |
2.64 GB
|
256.1 t/s | Fits in VRAM |
| unsloth/Qwen3-1.7B-GGUF | 1.72B | BF16 | Excellent |
4.45 GB
|
112.1 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-350M-GGUF | 0.35B | BF16 | Excellent |
1.51 GB
|
543.3 t/s | Fits in VRAM |
| Qwen/Qwen2.5-Coder-14B-Instruct-GGUF | 14.77B | Q5_K_M | Very good |
11.34 GB
|
36.8 t/s | Fits in VRAM |
| unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF | 8.19B | Q8_K_XL | Excellent |
11.44 GB
|
35.7 t/s | Fits in VRAM |
| bartowski/Nanbeige_Nanbeige4.3-33B-GGUF | 33.12B | IQ2_S | Low |
12.0 GB
|
34.7 t/s | Fits in VRAM |
| Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF | 1.54B | Q8_0 | Excellent |
2.67 GB
|
204.0 t/s | Fits in VRAM |
| bartowski/Qwen2.5-14B-Instruct-GGUF | 14.77B | Q5_K_L | Very good |
11.78 GB
|
35.2 t/s | Fits in VRAM |
| ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF | 31.58B | Q4_0 | Good |
18.42 GB
|
2.8 t/s | Offload |
| bartowski/Llama-3.3-70B-Instruct-GGUF | 70.55B | Q2_K_L | Low |
27.74 GB
|
2.0 t/s | Offload |
| unsloth/Qwen3-Coder-Next-GGUF | 79.67B | IQ3_XXS | Low |
27.7 GB
|
1.9 t/s | Offload |
"Fits in VRAM" = fast, fully on GPU. "Offload" = part on system RAM, slower. Speed is a rough estimate from the NVIDIA RTX 3060 12 GB's 360 GB/s memory bandwidth (offloaded models are limited by system RAM).
Frequently asked questions
What is the VRAM and memory bandwidth of the NVIDIA RTX 3060 12 GB?
The NVIDIA RTX 3060 12 GB features 12 GB of VRAM and a memory bandwidth of 360 GB/s (GDDR6 192-bit). In local language model inference, VRAM determines which model sizes fit on the card, while memory bandwidth dictates how many tokens per second the GPU generates.
What is the best local AI model to run on a NVIDIA RTX 3060 12 GB?
The best overall model for the NVIDIA RTX 3060 12 GB is Qwen/Qwen3-8B-GGUF using the recommended Q8_0 quantization (9.47 GB total memory). With 12 GB of VRAM, this GPU typically runs a 14B model at Q5, comfortably at full GPU speed. Check our guide to the best LLMs for 12 GB VRAM for details.
Can the NVIDIA RTX 3060 12 GB run 14B models locally?
Yes. The NVIDIA RTX 3060 12 GB's 12 GB VRAM is the exact sweet spot for running 14B models (such as Qwen 2.5 Coder 14B) in Q4_K_M quantization (~9.2 GB footprint) with full 4,096-token context entirely in VRAM.
NVIDIA RTX 3060 12 GB Head-to-Head Comparisons
Compare specs, memory bandwidth, and AI model capability against other graphics cards.
RTX 3060 12GB vs RTX 4060 8GB
Why 12 GB beats 8 GB for running 14B models natively without offloading.
RTX 3060 12GB vs RTX 3060 Ti 8GB
12 GB VRAM capacity vs faster 256-bit bus: which matters more for local AI?
RTX 3060 12GB vs RTX 4060 Ti 16GB
The sub-$300 12 GB champion against the entry-level 16 GB card.
RTX 2080 Ti 11GB vs RTX 3060 12GB
Used 11 GB with 616 GB/s bandwidth vs modern budget 12 GB.
RTX 3080 10GB vs RTX 3060 12GB
Blazing 760 GB/s bandwidth vs 12 GB capacity: why 14B models fail on 10 GB.
Intel Arc B580 12GB vs RTX 3060 12GB
Intel Battlemage 12GB challenger vs the established NVIDIA 12GB king.