Best AI Models for the RTX 3060 12GB
The classic budget local-AI card — what its 12GB really runs, with honest speed expectations
The best AI model for the RTX 3060 12GB is Phi-4 14B — ~9GB at Q4_K_M with a bandwidth ceiling of ~40 tokens/sec on this card (360 GB/s ÷ 9.1GB — a ceiling, not a measurement). The 3060’s 12GB is exactly what the 14B class needs: Qwen 2.5 Coder 14B for coding and DeepSeek-R1 14B for reasoning are the same ~9GB, so pick by task. For images, SDXL fits with the full ControlNet and LoRA toolkit. One honesty note: the RTX 4070 carries 40% more memory bandwidth (504 vs 360 GB/s), so every ceiling on this page is the lower of the two 12GB cards.
Models that run well on the RTX 3060 12GB
| Model | Size | Build | Memory used | Ceiling tok/s* |
|---|---|---|---|---|
| WINNERPhi-4 14B Best overall — punches far above its size on math and logic, with ~3GB left for the KV cache. ollama pull phi4 | 14B | Q4_K_M | ~9.0GB | ceiling ~40 tok/s (360 ÷ 9.1GB; a 4070’s is ~55) |
| Qwen 2.5 Coder 14B The real local-Copilot step up from the 7B coders — handles multi-file refactors they fumble. ollama pull qwen2.5-coder:14b | 14B | Q4_K_M | ~9.0GB | ceiling ~40 tok/s (360 ÷ 9.0GB) |
| DeepSeek-R1 14B Visible chain-of-thought — the hard-problems pick that fits this card. ollama pull deepseek-r1:14b | 14B | Q4_K_M | ~9.0GB | ceiling ~40 tok/s (360 ÷ 9.0GB) |
| Gemma 3 12B Best multilingual and prose — its smaller footprint buys ~1GB more context headroom than the 14Bs. ollama pull gemma3:12b | 12B | Q4_K_M | ~8.2GB | ceiling ~44 tok/s (360 ÷ 8.2GB) |
| SDXL 1.0 12GB fits SDXL plus ControlNet plus a LoRA stack at once — no memory flags needed. ComfyUI / Forge / Automatic1111 | 3.5B | FP16 | ~7GB | diffusion — compute-bound, not timed |
| Flux.1 dev FP8 Near-FP16 Flux quality fits the 3060; expect slower renders than a 4070, which has both more compute and ~40% more memory bandwidth. ComfyUI FP8 checkpoint | 12B | FP8 | ~11GB | diffusion — compute-bound, not timed |
| Llama 3.1 8B The speed lane — leaves ~7GB free for a genuinely long context or a second small model. ollama pull llama3.1:8b | 8B | Q4_K_M | ~5.0GB | ceiling ~73 tok/s (360 ÷ 4.9GB) |
* Ceiling tok/s = the device’s vendor-listed memory bandwidth ÷ the model’s file size as Ollama lists it — the rate a dense model cannot exceed, because every generated token reads the whole weight set once. We have not measured this hardware. Real-world generation usually lands at 50–80% of the ceiling and falls as the context grows; mixture-of-experts rows read only their active parameters, so no ceiling is quoted for them, and image models are compute-bound, so no render time is.
What won't fit on the RTX 3060 12GB
- ✗Codestral 22B (needs ~13GB) — Needs 16GB — for code on this card, Qwen 2.5 Coder 14B is the ceiling and it’s a good one.
- ✗gpt-oss:20b (needs ~14–16GB) — OpenAI’s open-weight MoE wants a 16GB card.
- ✗Qwen 2.5 32B (needs ~20GB) — Full-quality 32B is the reason people buy 24GB cards.
RTX 3060 12GB-specific notes
- →The 8GB trap: an RTX 3060 8GB exists, and it is a genuinely different card — the bus drops from 192-bit to 128-bit (360 → 240 GB/s), and 8GB loses this page’s entire 14B class. Used listings blur the two; confirm “12GB” explicitly before paying.
- →The laptop trap: the “RTX 3060 Laptop” has 6GB, not 12GB — half the memory and a completely different (much smaller) model list. Nothing on this page transfers to a 3060 laptop.
- →Speed honesty: every tok/s on this page is a bandwidth ceiling — 360 GB/s divided by the model’s file size — not a rate we recorded. The RTX 4070 has 504 GB/s, so its ceilings on the same models are 40% higher; real generation on either card lands below the ceiling and drops as context grows.
- →It’s still the value pick: a used 3060 12GB is the cheapest ticket into the 14B class, and at 170W with a single 8-pin it drops into basically any PC. Sanity-check used prices though — in the 2026 shortage everything is inflated (street prices ~50%+ over MSRP across tiers, per our GPU-prices guide), and if a 3060 costs close to a 4070, the 4070 is the strictly better card.
- →The newer RTX 4060 is not an upgrade for AI: 8GB vs 12GB, and 272 vs 360 GB/s. The 3060 12GB holds bigger models and generates faster on the bandwidth-bound work this page is about.
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Frequently asked questions
Is the RTX 3060 12GB still good for local AI?
Yes — arguably the best budget card for it. The entire 14B class (Phi-4, Qwen 3 14B, DeepSeek-R1 14B) runs at Q4 fully in its VRAM, with a bandwidth ceiling around 40 tok/s — comfortably past reading speed even at a fraction of that. The card is old; the 12GB is not — capacity determines what you can run, and 12GB is the cheapest way into the 14B tier.
How much slower is the 3060 than an RTX 4070 for LLMs?
By bandwidth, the 4070’s ceiling is 40% higher — ~55 vs ~40 tok/s on a 9GB 14B at Q4 (504 vs 360 GB/s ÷ 9GB). The model list is identical (both are 12GB cards); only the per-token ceiling differs. If reading-speed generation is enough for you, the 3060 saves real money.
RTX 3060 12GB vs 8GB — does it matter for AI?
Enormously. The 8GB variant loses the 14B class entirely (those models need ~9GB plus KV cache) and its narrower 128-bit bus cuts bandwidth by a third (240 vs 360 GB/s) — slower on the models it does fit. For AI, the 12GB variant is the only 3060 worth buying.
Can the RTX 3060 run Stable Diffusion and Flux?
Both. SDXL runs at 1024px with room for ControlNet and LoRAs. Flux.1 dev FP8 (~11GB) fits too — just expect slower renders than an RTX 4070. We have not timed either on this card.
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