What can NVIDIA GeForce RTX 3060 12GB run locally?

7 models fit comfortably on NVIDIA GeForce RTX 3060 12GB with 32 GB system RAM. 38 more run with tradeoffs. Top pick: Gemma 4 E2B (Effective 2B).

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 coding?

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
7 models

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

#1Gemma 4 E2B (Effective 2B)

gemma

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 5.4 GBHeadroom: 6.6 GBTTFT: fast

ollama run gemma4:e2b

194

tok/s

Weights

1.21 GB

KV cache

0.25 GB

Activations

2.11 GB

Runtime

1.80 GB

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

Model details →Run-on benchmark page →

#2Llama 3.2 3B Instruct

llama

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 6.1 GBHeadroom: 5.9 GBTTFT: noticeable

ollama run llama3.2:3b

129

tok/s

Weights

1.81 GB

KV cache

0.38 GB

Activations

2.14 GB

Runtime

1.80 GB

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

Model details →Run-on benchmark page →

#3Phi-3.5 Mini Instruct

3.8B

phi

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 6.7 GBHeadroom: 5.3 GBTTFT: noticeable

ollama run phi3.5:3.8b

102

tok/s

Weights

2.29 GB

KV cache

0.47 GB

Activations

2.16 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~766 ms (noticeable)

Model details →Run-on benchmark page →

#4Gemma 4 E4B (Effective 4B)

gemma

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 6.9 GBHeadroom: 5.1 GBTTFT: noticeable

ollama run gemma4:e4b

tok/s

Weights

2.42 GB

KV cache

0.50 GB

Activations

2.17 GB

Runtime

1.80 GB

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

Model details →Run-on benchmark page →

#5Qwen 3 4B

qwen

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 6.9 GBHeadroom: 5.1 GBTTFT: noticeable

ollama run qwen3:4b

tok/s

Weights

2.42 GB

KV cache

0.50 GB

Activations

2.17 GB

Runtime

1.80 GB

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

Model details →Run-on benchmark page →

#6Gemma 3 4B

gemma

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 6.9 GBHeadroom: 5.1 GBTTFT: noticeable

ollama run gemma3:4b

tok/s

Weights

2.42 GB

KV cache

0.50 GB

Activations

2.17 GB

Runtime

1.80 GB

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

Model details →Run-on benchmark page →

#7Phi-3.5 Vision

4.2B

phi

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 7.0 GBHeadroom: 5.0 GBTTFT: noticeable

tok/s

Weights

2.54 GB

KV cache

0.53 GB

Activations

2.17 GB

Runtime

1.80 GB

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

Model details →Run-on benchmark page →

Runs with tradeoffs
38 models

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

Gemma 3 1B

gemma

Commercial OK

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

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

ollama run gemma3:1b

388

tok/s

Weights

0.60 GB

KV cache

0.50 GB

Activations

8.22 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~202 ms (fast)

Model details →Run-on benchmark page →

Llama 3.2 1B Instruct

llama

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 11.6 GBHeadroom: 0.4 GBTTFT: fast

• Tight VRAM fit — only 0.4 GB headroom left for context growth

ollama run llama3.2:1b

220

tok/s

Weights

1.06 GB

KV cache

0.50 GB

Activations

8.25 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~202 ms (fast)

Model details →Run-on benchmark page →

CodeGemma 7B

gemma

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 2.8 GBTTFT: noticeable

• Tight VRAM fit — only 2.8 GB headroom left for context growth

ollama run codegemma:7b

tok/s

Weights

4.23 GB

KV cache

0.88 GB

Activations

2.26 GB

Runtime

1.80 GB

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

Model details →Run-on benchmark page →

Qwen 2.5 7B Instruct

qwen

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 8.4 GBHeadroom: 3.6 GBTTFT: noticeable

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

ollama run qwen2.5:7b

tok/s

Weights

4.23 GB

KV cache

0.12 GB

Activations

2.26 GB

Runtime

1.80 GB

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

Model details →Run-on benchmark page →

DeepSeek Coder V2 Lite (16B)

16B

deepseek

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 28.1 GBHeadroom: 3.1 GBTTFT: slow

• Partial CPU offload: ~57% of layers run on CPU

ollama run deepseek-coder-v2:16b

tok/s

Weights

9.66 GB

KV cache

8.00 GB

Activations

8.68 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~3225 ms (slow)

Model details →Run-on benchmark page →

Llama 3.1 8B Instruct

llama

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 2.8 GBTTFT: noticeable

• Tight VRAM fit — only 2.8 GB headroom left for context growth

ollama run llama3.1:8b

tok/s

Weights

4.83 GB

KV cache

0.27 GB

Activations

2.29 GB

Runtime

1.80 GB

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

Model details →Run-on benchmark page →

Qwen 3 8B

qwen

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 9.9 GBHeadroom: 2.1 GBTTFT: noticeable

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

ollama run qwen3:8b

tok/s

Weights

4.83 GB

KV cache

1.00 GB

Activations

2.29 GB

Runtime

1.80 GB

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

Model details →Run-on benchmark page →

Codestral 22B

22B

mistral

Quant: Q4_K_MContext: 2,048VRAM: 20.5 GBHeadroom: 10.7 GBTTFT: slow

• Partial CPU offload: ~42% of layers run on CPU

ollama run codestral:22b

tok/s

Weights

13.28 GB

KV cache

2.75 GB

Activations

2.71 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~4435 ms (slow)

Model details →Run-on benchmark page →

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: 40 new tradeoff

• Llama 3.1 8B Instruct
• Qwen 3 30B-A3B
• Qwen 2.5 Coder 32B Instruct
• Llama 3.3 70B Instruct

Shop this upgrade↗

Upgrade to NVIDIA GeForce RTX 4080 Super

~$1099

16 GB VRAM (vs your 12 GB) plus a bandwidth jump from ~360 GB/s to ~736 GB/s.

Unlocks: 10 new comfortable

• Gemma 3 1B
• Llama 3.2 1B Instruct
• DeepSeek R1 Distill Qwen 7B
• Llama 3.1 8B 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: 13 new comfortable

• Gemma 3 1B
• Llama 3.2 1B Instruct
• Llama 3.1 Nemotron Nano 8B
• Mistral 7B Instruct v0.3

Shop this upgrade↗

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Won't run
top 5 popular models

Need more memory than you have. Shown for orientation.

Qwen 3 235B-A22B

235B

qwen

Commercial OK