RTX 3060 12GB for AI (2026): What It Runs, Real Speeds, Price and Is It Still Worth It

The RTX 3060 12GB is five years old, yet it is still one of the most recommended cards for local AI. The reason is simple: it is the cheapest NVIDIA card with 12 GB of VRAM, and VRAM decides which models you can run.

The catch in late 2026 is price. The memory shortage has pushed used 3060s from under $200 in May to around $345-$450 in November.

This guide shows what the card actually runs, real benchmark numbers, the best settings for Ollama and ComfyUI, and whether it is still worth buying against an RTX 5060 Ti 16GB or a used RTX 3090.

RTX 3060 12GB Specs for AI

RTX 3060 12GB Price in November 2026

These figures come from GPUDojo’s price tracker, which logs eBay, Amazon and Newegg listings (updated 8 November 2026).

Asking prices are not sale prices, and many sellers list high and wait. Watch completed sales before you buy. The rise follows the wider memory crunch, which my guide to why GPU prices are so high in 2026 explains.

LLM Speed: Real Tokens per Second

I haven’t benchmarked a 3060 myself, so these numbers are from two public sources: the llama.cpp CUDA scoreboard and Hardware Corner’s March 2026 tests.

Anything above about 10 tokens per second feels fine for chat, so every one of these is comfortable. The numbers drop as context grows, which matters for long documents and coding agents.

For scale, the same llama.cpp scoreboard shows the RTX 5060 Ti 16GB at 93 t/s, the RTX 3090 at 162 t/s and the RTX 5070 Ti at 182 t/s on the same 7B test.

Which LLMs Run on 12 GB

Leave 1-2 GB for context and the desktop. Sizes are typical GGUF downloads.

MoE models are the 3060’s secret weapon. Only a few billion parameters are used per token, so part of the model can sit in system RAM without killing speed. You’ll want 32 GB of RAM or more for this; my RAM for local AI guide explains how much.

For picks by task, see my list of best Ollama models by VRAM and the Gemma 4 local guide.

Image and Video Generation

The 12 GB is what makes image work possible at all, since many 8 GB cards run out of memory on SDXL and FLUX. For the model files that suit 12 GB, see my Qwen-Image 2.1 GGUF on 8 GB and 12 GB guide.

RTX 3060 12GB vs 5060 Ti 16GB vs 3090 vs 4060 Ti

The 3060 actually beats the 4060 Ti 8GB at text generation, because it has more bandwidth and more memory. The 5060 Ti 16GB adds 4 GB, FP4 support and better speed, but costs about twice as much.

My RTX 5060 Ti 16GB for AI guide covers that card, and my used RTX 3090 guide covers the 24 GB route.

Best Settings for Ollama, LM Studio and ComfyUI

Ollama

1. Install the latest NVIDIA driver and Ollama (see my Ollama install guide).
2. Pull a Q4_K_M model, for example ollama run qwen3:14b.
3. Check it’s on the GPU with ollama ps. You want 100% GPU. If it says something like 30%/70% CPU/GPU, the model plus context is too big.
4. Keep context sensible: set OLLAMA_CONTEXT_LENGTH=8192 for 14B models. Bigger contexts push layers to the CPU.
5. Turn on flash attention with OLLAMA_FLASH_ATTENTION=1 and use OLLAMA_KV_CACHE_TYPE=q8_0 to roughly halve the context memory.

LM Studio and llama.cpp

• Set GPU offload to the maximum for models under about 10 GB.
• For MoE models that don’t fit, keep attention on the GPU and move expert layers to the CPU. In llama.cpp use --n-cpu-moe with a number of layers, raising it until the model loads.
• Enable flash attention and a Q8 KV cache for long contexts.

ComfyUI

• Use GGUF versions of FLUX-class models with the ComfyUI-GGUF nodes.
• Load the text encoder in a smaller format, or on the CPU, to free VRAM.
• Close other GPU-heavy apps (games, video editors, many browser tabs), and if you hit out-of-memory errors, start ComfyUI with --lowvram.

Tip: plug your monitor into the motherboard’s video output if your CPU has integrated graphics. That frees the few hundred MB of VRAM Windows uses for the desktop.

Buying a Used RTX 3060: Checklist

1. Confirm it says 12GB. There are 8 GB versions and the 3060 Ti is also 8 GB. Ask for a GPU-Z screenshot showing 12288 MB and a 192-bit bus.
2. Check the price against completed sales, not asking prices.
3. Ask about mining or heavy use. Mining cards often work fine, but check fan noise and temperatures.
4. Test on arrival: run a 15-minute stress test or a long ComfyUI batch and watch temperatures in HWiNFO or nvidia-smi. Under about 80 °C is normal.
5. Check VRAM: load a model that fills 11 GB and run it for a while. Errors or crashes here can point to bad memory.
6. Use buyer protection such as eBay’s money-back guarantee.

My guide to checking GPU VRAM and usage shows how to confirm the memory size on Windows and Linux.

Two RTX 3060s for 24 GB?

It works for LLMs. llama.cpp, Ollama and LM Studio can split a model across two cards, giving you about 24 GB for models like Qwen3.8-27B at Q4.

Speed is still limited by each card’s 360 GB/s, and you need a motherboard with two x16-size slots, good airflow and a 750 W PSU.

In ComfyUI, two cards don’t combine into one 24 GB pool for a single image; my ComfyUI multi-GPU guide explains what does work. At today’s prices, two used 3060s cost about as much as one new 5060 Ti 16GB.

Is It Still Worth It?

For other budgets, my best GPU for local LLMs guide ranks every card by VRAM and street price.

Troubleshooting

Ollama is slow and the GPU is barely used

The model or context is too big, so part of it runs on the CPU. Run ollama ps, then choose a smaller quant or reduce the context length until it shows 100% GPU.

CUDA out of memory in ComfyUI

Switch to a GGUF model, lower the resolution or batch size, offload the text encoder, or start ComfyUI with --lowvram.

An FP8 or FP4 model won’t load or is very slow

Ampere cards have no FP8 or FP4 hardware. Download a GGUF or FP16 version instead.

The card shows only 8 GB

You have the 8 GB version, or a 3060 Ti. Check the exact model in GPU-Z or with nvidia-smi.

Crashes during long runs

Check temperatures and the PSU. Clean the fans, update the driver, and if it’s a used card, test the VRAM with a full-memory load.

Frequently Asked Questions

Is the RTX 3060 12GB good for AI in 2026?

Yes as a budget card. It runs 7B-14B language models fully in VRAM at 30-55 tokens per second and handles SDXL and quantized FLUX-class image models. It is only good value at about $300 or less.

How much does a used RTX 3060 12GB cost now?

On 8 November 2026 the cheapest used listing on eBay US was about $345, with typical asking prices around $450. New cards were about $460.

What is the biggest LLM an RTX 3060 12GB can run?

About 14B parameters at Q4 fully on the GPU. MoE models such as gpt-oss-20b and Gemma 4 26B A4B also run with some layers offloaded to system RAM.

Can an RTX 3060 run FLUX?

Yes, with GGUF Q4 or Q5 versions and the text encoder offloaded. Expect roughly 30-60 seconds or more per image.

RTX 3060 12GB or RTX 4060 Ti 8GB for AI?

The RTX 3060 12GB. The extra 4 GB of VRAM matters more for AI than the 4060 Ti’s newer architecture, and the 3060 is also faster at text generation in the llama.cpp scoreboard.

RTX 3060 12GB or RTX 5060 Ti 16GB?

The 5060 Ti 16GB is faster and fits bigger models, but costs about $770 new. Choose the 3060 only if you can get it for about $300 or less.

Does the RTX 3060 support FP8 or FP4?

No. It is an Ampere card with FP16 and BF16 support. Use GGUF or FP16 models.

Final Thoughts

The RTX 3060 12GB is still the cheapest sensible way into local AI on an NVIDIA card. It runs the 7B-14B models most people actually use, plus SDXL and quantized image models.

The only thing that has changed is price. At around $300 it is a great buy; at $450 a 16 GB card is the smarter long-term choice. Whatever you choose, my VRAM requirements guide will tell you exactly what fits.

Sources

• llama.cpp: performance on NVIDIA CUDA scoreboard
• Hardware Corner: RTX 3060 12GB LLM benchmarks (March 2026)
• GPUDojo: RTX 3060 12GB used price and history (November 2026)
• RunAIHome: used RTX 3060 12GB for local AI (August 2026)
• NVIDIA: GeForce RTX 3060 family specs