Ranking updated on 09/10/2026
The RTX 3060 12 GB is the most popular budget card for running LLMs locally. 12 GB of VRAM handles 7–9B models in Q4/Q5 smoothly. Here are the best choices.
RTX 3060 12GB : purchasing alternative available for local AI — RTX 5060 Ti 16 GB :
Which PC should you choose for your budget? Our picks from €800 to €3,500 →
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4B effective multimodal (text+image+audio). 140 languages. For laptops and edge devices.
ollama run gemma4:e4b
Dense 8B Apache 2.0, 12 languages including FR, 131k context, GQA 32Q/8KV. MMLU 73.84, HumanEval 85.37. Released April 29, 2026.
# HuggingFace : ibm-granite/granite-4.1-8b
Gemma 4 12B (Google): dense multimodal model (text, vision, audio), 256k context, ~7 GB Q4 VRAM. Apache 2.0, multilingual.
# HuggingFace : google/gemma-4-12B
Dense 14B with hybrid thinking. Equals Qwen 2.5 32B Based on STEM/code.
ollama run qwen3:14b
MIT 14B reasoner. Beats R1-Distill-Llama-70B on AIME/GPQA with 50× fewer parameters.
ollama run phi4-reasoning:14b
Exceptional reasoning for its size. STEM-focused.
ollama run phi4:14b
Coding 14B. HumanEval 89.6, LiveCodeBench 37.1. VRAM sweet spot for self-hosted coding.
ollama run qwen2.5-coder:14b
Distilled R1 Qwen 14B. AIME24 69.7, MATH-500 93.9. Outperforms o1-mini on many benchmarks.
ollama run deepseek-r1:14b
| Rank | Model | Params | Q4 VRAM | Context | License | On RTX 3060 12GB |
|---|---|---|---|---|---|---|
| #1 | Gemma 4 E4B | 4B | 10 GB | 128 000 | Apache 2.0 | 14 tok/s · Q8 |
| #2 | Granite 4.1 8B Instruct | 8B | 5 GB | 131 072 | Apache 2.0 | 12 tok/s · Q8 |
| #3 | Gemma 4 12B | 12B | 7 GB | 262 144 | Apache 2.0 | 18 tok/s · Q5_K_M |
| #4 | Qwen 3 14B | 14B | 9 GB | 131 072 | Apache 2.0 | 6 tok/s · Q5_K_M |
| #5 | Phi-4 Reasoning 14B | 14B | 9 GB | 32 768 | MIT | 6 tok/s · Q5_K_M |
| #6 | Phi-4 14B | 14B | 9 GB | 16 384 | MIT | 6 tok/s · Q5_K_M |
| #7 | Qwen 2.5 Coder 14B Instruct | 14B | 9 GB | 131 072 | Apache 2.0 | 6 tok/s · Q5_K_M |
| #8 | DeepSeek R1 Distill Qwen 14B | 14B | 9 GB | 131 072 | MIT | 6 tok/s · Q5_K_M |
Your private, free ChatGPT on your machine in 1 hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
Free memo
Get the memo VRAM → best coding model → Ollama command (one screen, copy and paste). Then switch to the Copilote Local kit for a setup that actually works.
The Local Copilot kit — the Ollama + Cline + Aider configs are ready to paste, with tuned Modelfiles, troubleshooting, and lifetime online access →No spam. Unsubscribe in 1 click. Your data stays with us (never resold).
Your card → the best coding model to run locally, and the exact Ollama command:
| Your VRAM | Typical GPUs / Macs | Recommended coding model | Command Ollama |
|---|---|---|---|
| 8 GB | RTX 4060 / 3060 · M1-M2 16 GB | Qwen 3.5 9B (Q4, 6.6 GB — 256k context) | ollama run qwen3.5:9b |
| 12 GB | RTX 3060 12 GB / 4070 / 5070 | Qwen 3.5 9B (Q8, 11 GB) or Gemma 4 12B (7.6 GB) | ollama run qwen3.5:9b-q8_0 |
| 16 GB | RTX 5070 Ti / 4080 / 5080 · RX 9070 XT · M4 24 GB | Devstral 24B (Q4, 14 GB) — coding-agent specialist | ollama run devstral:24b |
| 24 GB | RTX 3090 / 4090 · RX 7900 XTX · M4 Pro 48 GB | Qwen 3.8 27B (Q4, 18 GB) — the “close to Copilot” option | ollama run qwen3.8:27b |
| 32 GB | RTX 5090 | Qwen 3.6 35B-A3B (Q4, 23 GB) — fast MoE | ollama run qwen3.6:35b |
| 48 GB+ | Mac M4 Max 64 GB · M2 Ultra 128 GB | Qwen3-Coder 30B-A3B (Q8, 32 GB — 256k context) | ollama run qwen3-coder:30b-a3b-q8_0 |
-base : ollama run qwen2.5-coder:7b-base — it’s still the reference for this specific use case. ⚠️ Qwen 3.8: its reasoning is set very high by default and it “overthinks” simple requests — lower it to low (or turn it off) on first launch. ⚠️ License trap: Codestral 22B = Mistral Non-Production License → prohibited for coding at work. Qwen 3.5/3.8, Gemma 4, and Devstral are Apache 2.0. 💡 Running out of memory? Keep ~1.5 GB of VRAM free for context, or drop down one quantization level.🔌 To connect it to VS Code: Cline (multi-file agent), Aider (CLI) or Tabby/Twinny (FIM autocomplete) — they all connect to Ollama locally. The kit Local Copilot — ready-to-paste configs + tested setup — is available: /copilote-local.
Filter: models that fit in Q4_K_M within 12 GB. Bonus for those using at least 40% of VRAM—we avoid recommending a model that is too small and underuses the card.
Criteria considered:
The scoring is fully transparent: see our methodology for details on VRAM/tokens/sec calculations.
Can you do serious RAG on RTX 3060 12 GB?
Yes with Llama 3.1 8B (128k context) or Qwen 2.5 7B (131k). In Q4, the model plus ~32k RAG context fits in 12 GB. For context > 100k, switch to Q4_0 or limit the batch.
RTX 3060 vs. RX 6700 XT for LLMs?
CUDA is much better supported (Ollama, llama.cpp, and vLLM are all optimized). AMD works through ROCm but with more complex setups. For LLMs, NVIDIA remains the obvious choice in 2026.
Which Q should you choose on 12 GB?
Q5_K_M for a 7–8B model (uses ~7 GB, leaving room for a large context). Q4_K_M for a 12B model (Mistral Nemo). Avoid Q3/Q2—the degradation is visible.
Can you run a 12B in real time?
Yes—Mistral Nemo 12B in Q4_K_M (~7 GB) delivers 10–15 tokens/sec on a 3060. Usable for chat, a little slow for real-time editing.