12GB of VRAM runs more than you'd think — from snappy 7B models to a DeepSeek R1 distill. Here's exactly which local models the RTX 3060 12GB can run, and how fast.
More than its budget price suggests. With 12GB of [VRAM](/how-much-vram-do-you-need-to-run-ai-models-2026), the RTX 3060 runs the entry-to-mid range of local models well: 7-8B models fly (~42 tokens/second), 9B models are excellent (Qwen 9B ~38 tok/s), 13-14B models are workable (roughly 10-22 tok/s), and it even runs the [DeepSeek R1 8B distill](/can-the-rtx-3060-12gb-run-deepseek-r1-2026) for reasoning. It fits every 7B model at [Q4/Q5](/which-quantization-should-you-use-q4-q5-q8-2026) and most 13B models at Q4. What it can't do is 30B+ models — those need more VRAM. Here's the full list by size, with real numbers.
The full list, by model size
Here's what fits and how it performs. 7-8B — the sweet spot for speed. These run great: 8B models around 42 tok/s, snappy enough for smooth chat and coding autocomplete; every 7B fits at Q4/Q5. 9B — excellent.Qwen 9B at ~38 tok/s is arguably the best all-round model for this card — capable and fast. 13-14B — workable. These fit (most 13B at Q4) and run in the low double digits — a 14B model does about 22 tok/s generation at 16K context, or lower at longer context; usable but not snappy. Reasoning — yes, the [8B distill](/can-the-rtx-3060-12gb-run-deepseek-r1-2026). The card runs the [DeepSeek R1](/is-deepseek-r1-worth-running-locally-2026) 8B distill at ~10-12 tok/s, giving you real step-by-step reasoning on a budget. ~20B and up — the ceiling. Bigger models like Mistral Small run slowly (~18 tok/s) and are near the limit; 30B+ won't fit. For the best experience, stick to 7-9B models at Q4/Q5, where the 3060 feels genuinely good.
RTX 3060 12GB model support
7-8B
Model size
8B ~42 tok/s
Example + speed
Flies
9B
Model size
Qwen 9B ~38 tok/s
Example + speed
Excellent
13-14B
Model size
14B ~22 tok/s (16K)
Example + speed
Workable
R1 8B distill
Model size
~10-12 tok/s
Example + speed
Reasoning on a budget
30B+
Model size
Qwen3-Coder 32B
Example + speed
No (needs 24GB)
Model size
Example + speed
Runs?
7-8B
8B ~42 tok/s
Flies
9B
Qwen 9B ~38 tok/s
Excellent
13-14B
14B ~22 tok/s (16K)
Workable
R1 8B distill
~10-12 tok/s
Reasoning on a budget
30B+
Qwen3-Coder 32B
No (needs 24GB)
7-9B models are the RTX 3060's sweet spot (~38-42 tok/s); 13-14B are workable; 30B+ is the hard line. · Unsplash
How to get the most out of 12GB
A few habits make the RTX 3060 12GB shine. Use [Q4_K_M or Q5](/which-quantization-should-you-use-q4-q5-q8-2026): it fits bigger models with minimal quality loss and leaves room for context. Favour 7-9B models: they're the card's sweet spot — fast and capable — and cover most real local-AI use (chat, coding, RAG, summarization). Mind context length: long context grows the KV cache and eats your 12GB, so keep it reasonable on 13B+ models. Use [Ollama or LM Studio](/ollama-vs-lm-studio-which-should-you-use-2026) to handle loading and quantization, and [size any model in the VRAM calculator](/tools/vram-calculator) before pulling it. Do that and the 3060 runs the 7-13B range well — plenty for getting into local AI. When you outgrow it (wanting 30B+ or long context), that's the cue to move to 16GB or 24GB. But for the cheapest way to run genuinely useful models at home, the 3060 delivers.
Quick answers
What size LLM can the RTX 3060 12GB run?
It comfortably runs models up to about 13-14B parameters at Q4 quantization, with 7-9B models being its sweet spot. Every 7B model fits at Q4/Q5, and most 13B models fit at Q4. In speed terms, 8B models run around 42 tokens per second, Qwen 9B around 38 tok/s, and 14B models in the low double digits (about 22 tok/s generation at 16K context). It also runs the DeepSeek R1 8B distill for reasoning. What it can't run is 30B+ parameter models, which need a 24GB card. So for 7-14B models — which covers most everyday local-AI use — the RTX 3060 12GB is capable; larger models are out of reach.
Can the RTX 3060 run 13B models?
Yes, workably. Most 13B models fit on the RTX 3060 12GB at Q4 quantization, and a 14B model runs at roughly 22 tokens per second generation at a 16K context window — usable, though not as snappy as the 7-9B models the card excels at. Longer context will slow it further and use more VRAM, so keep context reasonable for 13B+ models. For the best experience, 7-9B models are the sweet spot (around 38-42 tok/s), but if you want to run a 13-14B model occasionally, the 3060 can do it at acceptable speeds. For consistently fast 13-14B performance, a faster 16GB card is better.
Can the RTX 3060 run reasoning models like DeepSeek R1?
Yes — it runs the DeepSeek R1 8B distill at roughly 10-12 tokens per second, giving you genuine step-by-step reasoning on a budget card. The 8B distill fits comfortably in 12GB and keeps much of R1's reasoning ability, so it's a great way to try local reasoning cheaply. What the 3060 can't run is the larger R1 distills — the 14B is tight and the 32B needs 24GB — so you're limited to the 8B distill for reasoning. That's still very capable for math, logic, and step-by-step problems. If you want the stronger 14B or 32B reasoning distills, you'd need a 16GB or 24GB card.
