What can NVIDIA GeForce RTX 3060 12GB run locally?

4 models fit comfortably on NVIDIA GeForce RTX 3060 12GB with 32 GB system RAM. 27 more run with tradeoffs. Top pick: Moondream 2.

What's the recommended runner for this hardware?

llama.cpp / Ollama (CUDA). This is the runner that gives best tokens-per-second on NVIDIA GeForce RTX 3060 12GB based on windows support and the GPU backend.

What can NVIDIA GeForce RTX 3060 12GB run for vision?

Build: NVIDIA GeForce RTX 3060 12GB + — + 32 GB RAM (windows)

Memory: 12 GB VRAM + 32 GB system RAM

Runner: llama.cpp / Ollama (CUDA)

Any Chat Coding Agents Reasoning Vision Long context Creative

Runs comfortably
4 models

Ranked by fit for vision use case + predicted speed. Click a row for VRAM breakdown.

#1Moondream 2

1.9B

other

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 3.3 GBHeadroom: 8.7 GBTTFT: fast

204

tok/s

Estimated

Weights

1.20 GB

KV cache

0.24 GB

Activations

0.06 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~383 ms (fast)

Model details →

#2Qwen 2.5-VL 3B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 5.4 GBHeadroom: 6.6 GBTTFT: noticeable

129

tok/s

Estimated

Weights

2.00 GB

KV cache

1.50 GB

Activations

0.11 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~605 ms (noticeable)

Model details →

#3Gemma 4 E2B (Effective 2B)

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 5.1 GBHeadroom: 6.9 GBTTFT: fast

ollama run gemma4:e2b

110

tok/s

Estimated

Weights

2.20 GB

KV cache

1.00 GB

Activations

0.12 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~403 ms (fast)

Model details →

#4Phi-3.5 Vision

4.2B

phi

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 6.5 GBHeadroom: 5.5 GBTTFT: noticeable

tok/s

Estimated

Weights

2.50 GB

KV cache

2.10 GB

Activations

0.13 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~847 ms (noticeable)

Model details →

Runs with tradeoffs
27 models

Tight VRAM, partial CPU offload, or context-limited.

LLaVA 1.6 Mistral 7B

other

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 10.0 GBHeadroom: 2.0 GBTTFT: noticeable

• Tight VRAM fit — only 2.0 GB headroom left for context growth

tok/s

Estimated

Weights

4.50 GB

KV cache

3.50 GB

Activations

0.23 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1411 ms (noticeable)

Model details →

LLaVA-OneVision 7B

other

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 10.0 GBHeadroom: 2.0 GBTTFT: noticeable

• Tight VRAM fit — only 2.0 GB headroom left for context growth

tok/s

Estimated

Weights

4.50 GB

KV cache

3.50 GB

Activations

0.23 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1411 ms (noticeable)

Model details →

Qwen 2-VL 7B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 10.1 GBHeadroom: 1.9 GBTTFT: noticeable

• Tight VRAM fit — only 1.9 GB headroom left for context growth

tok/s

Estimated

Weights

4.60 GB

KV cache

3.50 GB

Activations

0.24 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1411 ms (noticeable)

Model details →

Qwen 2.5-VL 7B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 9.9 GBHeadroom: 2.1 GBTTFT: noticeable

• Tight VRAM fit — only 2.1 GB headroom left for context growth

tok/s

Estimated

Weights

4.40 GB

KV cache

3.50 GB

Activations

0.23 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1411 ms (noticeable)

Model details →

Gemma 4 E4B (Effective 4B)

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 3.6 GBTTFT: noticeable

• Tight VRAM fit — only 3.6 GB headroom left for context growth

ollama run gemma4:e4b

tok/s

Estimated

Weights

4.40 GB

KV cache

2.00 GB

Activations

0.23 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~806 ms (noticeable)

Model details →

Gemma 3 4B

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 3.6 GBTTFT: noticeable

• Tight VRAM fit — only 3.6 GB headroom left for context growth

ollama run gemma3:4b

tok/s

Estimated

Weights

4.40 GB

KV cache

2.00 GB

Activations

0.23 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~806 ms (noticeable)

Model details →

MiniCPM-V 3 8B

minicpm

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 0.9 GBTTFT: noticeable

• Tight VRAM fit — only 0.9 GB headroom left for context growth

tok/s

Estimated

Weights

5.00 GB

KV cache

4.00 GB

Activations

0.26 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1613 ms (noticeable)

Model details →

MiniCPM-V 2.6 8B

minicpm

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 0.9 GBTTFT: noticeable

• Tight VRAM fit — only 0.9 GB headroom left for context growth

tok/s

Estimated

Weights

5.00 GB

KV cache

4.00 GB

Activations

0.26 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1613 ms (noticeable)

Model details →

What if you upgraded?

Hypothetical scenarios. We re-ran the compatibility engine for each.

+32 GB system RAM

~$80–150

Doubles your CPU-offload working set. Helps when models don't quite fit in VRAM.

Unlocks: 63 new comfortable, 174 new tradeoff

• Qwen 3 0.6B
• Llama 3.2 3B Instruct
• Qwen 3 1.7B
• Qwen 2.5 7B Instruct

Shop this upgrade↗

Upgrade to NVIDIA GeForce RTX 4070 Ti Super

~$829

16 GB VRAM (vs your 12 GB) plus a bandwidth jump from ~360 GB/s to ~672 GB/s.

Unlocks: 138 new comfortable

• Qwen 3 0.6B
• Llama 3.1 8B Instruct
• Qwen 3 8B
• Llama 3.2 3B Instruct

Shop this upgrade↗

Add a second NVIDIA GeForce RTX 3060 12GB

~$249

Tensor parallelism splits the model across both cards, effectively doubling VRAM. Bandwidth doesn't double — runs ~1.5× the single-card speed in practice.

Unlocks: 180 new comfortable

• Qwen 3 0.6B
• Llama 3.1 8B Instruct
• Qwen 3 8B
• Gemma 4 12B

Shop this upgrade↗

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Won't run
top 5 popular models

Need more memory than you have. Shown for orientation.

DeepSeek V4 Pro (1.6T MoE)

1600B

deepseek

Commercial OK