Desktop GPU · NVIDIA
What AI models can a GeForce RTX 2060 12GB run?
12 GB is the point where local models stop being a demo. The popular 7B to 14B models fit with room for a real conversation, and a 32B fits if you accept a lower quantization. At 336 GB/s there is enough bandwidth to keep generation responsive for anything that fits.
The short answer
Assuming an 8k context window and default settings, these are the models worth downloading first.
Best all-rounder
Gemma 4 12B Instruct
Q6_K · 10.84 GB · about 23.7 tokens/s
Best for code
Qwen2.5 Coder 14B Instruct
Q4_K_M · 9.81 GB · about 26.4 tokens/s
Largest that still runs well
Qwen3 14B
14.77B parameters · Q5_K_M · about 23.5 tokens/s
Fastest useful answer
LFM2.5 8B A1B
about 87 tokens/s · 8.81 GB
See how fast it feels
GeForce RTX 2060 12GB running Gemma 4 12B Instruct at Q6_K
YouWhy does my model use more memory when the conversation gets longer?
Reading your question
Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU.
Where it sits
With 12 GB it belongs to the 12 GB tier. The 12 GB guide explains what that tier runs and where it runs out, and the bandwidth guide explains why 336 GB/s decides how fast it answers.
Our indicative price for this card is $160 used, reviewed 2026-09-01. That is 7.5 GB of memory and 210 GB/s of bandwidth for every $100. Prices move, so treat these as a place to start a search and not a quote.
Devices in a similar price band, fastest memory first:
| Device | Price | Memory | Bandwidth | Best all-round pick |
|---|---|---|---|---|
| Arc A750 | $200 used | 8 GB | 512 GB/s | Qwen3.5 9B, 48.3 t/s |
| GeForce GTX 1080 Ti | $150 used | 11 GB | 484 GB/s | Qwen3.5 9B, 40.3 t/s |
| GeForce RTX 3060 12GB | $200 used | 12 GB | 360 GB/s | Gemma 4 12B Instruct, 25.4 t/s |
| GeForce RTX 3050 8GB | $170 used | 8 GB | 224 GB/s | Qwen3.5 9B, 27.4 t/s |
Every model, scored on this device
Each row uses the highest-quality quantization that both fits and stays conversational. Speed is a single-stream estimate at 8k context.
| Model | Params | Verdict | Download | Memory used | Speed | Max context |
|---|---|---|---|---|---|---|
| Gemma 4 12B Instruct Gemma | 11.96B | Just fits | Q6_K | 10.84 GB | 23.7 t/s | 8k |
| Qwen3.5 9B Qwen | 9.65B | Runs great | Q8_0 | 9.25 GB | 28 t/s | 64k |
| Qwen3 14B Qwen | 14.77B | Just fits | Q5_K_M | 10.96 GB | 23.5 t/s | 8k |
| Gemma 3 12B Instruct Gemma | 12.19B | Just fits | Q6_K | 10.52 GB | 24.4 t/s | 16k |
| Gemma 4 E4B Instruct Gemma | 8B | Runs great | Q8_0 | 8.42 GB | 30.7 t/s | 128k |
| Granite 4.1 8B Granite | 8.79B | Just fits | Q8_0 | 10.35 GB | 24.9 t/s | 8k |
| Qwen2.5 14B Instruct Qwen | 14.77B | Runs great | Q4_K_M | 9.81 GB | 26.4 t/s | 8k |
| Qwen3 8B Qwen | 8.19B | Runs great | Q8_0 | 9.02 GB | 28.7 t/s | 16k |
| Qwen2.5 Coder 14B Instruct Qwen | 14.77B | Runs great | Q4_K_M | 9.81 GB | 26.4 t/s | 8k |
| Qwen2.5 VL 7B Instruct Qwen | 8.29B | Runs great | Q8_0 | 8.49 GB | 30.5 t/s | 32k |
| Phi 4 Phi | 14.66B | Runs great | Q4_K_M | 9.96 GB | 26 t/s | 8k |
| Mistral Nemo Instruct 2407 Mistral | 12.25B | Just fits | Q6_K | 10.53 GB | 24.5 t/s | 8k |
| Qwen3.5 27B Qwen | 27.78B | Fits only at 2 to 3 bits | IQ3_XXS ! | 10.53 GB | 24.5 t/s | 16k |
| Qwen2.5 7B Instruct Qwen | 7.62B | Runs great | Q8_0 | 7.83 GB | 33.2 t/s | 32k |
| Llama 3.1 8B Instruct Llama | 8.03B | Runs great | Q8_0 | 8.83 GB | 29.3 t/s | 16k |
| Qwen2.5 Coder 7B Instruct Qwen | 7.62B | Runs great | Q8_0 | 7.83 GB | 33.2 t/s | 32k |
| Gemma 2 9B Instruct Gemma | 9.24B | Runs great | Q6_K | 9.42 GB | 27.4 t/s | 8k |
| OLMo 3 7B Instruct OLMo | 7.3B | Runs great | Q8_0 | 9.72 GB | 26.5 t/s | 16k |
| Internlm3 8B Instruct InternLM | 8.8B | Runs great | Q8_0 | 8.97 GB | 28.9 t/s | 32k |
| Mistral 7B Instruct V0.3 Mistral | 7.25B | Runs great | Q8_0 | 8.45 GB | 30.7 t/s | 16k |
| DeepSeek Coder V2 Lite Instruct DeepSeek | 15.71B (2.74B active) | Just fits | Q5_K_M | 10.84 GB | 70.7 t/s | 16k |
| Gemma 4 E2B Instruct Gemma | 5.12B | Runs great | Q8_0 | 5.44 GB | 48.3 t/s | 128k |
| Qwen3.5 4B Qwen | 4.66B | Runs great | Q8_0 | 5.27 GB | 50.4 t/s | 128k |
| Mistral 7B Instruct V0.2 Mistral | 7.24B | Runs great | Q8_0 | 8.44 GB | 30.8 t/s | 16k |
| DeepSeek Coder 7B Instruct V1.5 DeepSeek | 6.91B | Just fits | Q8_0 | 10.58 GB | 24.3 t/s | 4k |
| Mistral Small 24B Instruct 2501 Mistral | 23.57B | Fits only at 2 to 3 bits | IQ3_XXS ! | 9.85 GB | 26.3 t/s | 16k |
| DeepSeek Coder 6.7B Instruct DeepSeek | 6.74B | Just fits | Q8_0 | 10.94 GB | 23.5 t/s | 8k |
| LFM2.5 8B A1B Liquid | 8.47B (1.57B active) | Runs great | Q8_0 | 8.81 GB | 87 t/s | 64k |
| Qwen3 4B Qwen | 4.02B | Runs great | Q8_0 | 5.46 GB | 48.4 t/s | 32k |
| Codestral 22B V0.1 Mistral | 22.25B | Fits only at 2 to 3 bits | IQ3_XXS ! | 10.08 GB | 25.7 t/s | 8k |
Show the other 56 models
| Gemma 3 4B Instruct | 4.3B | Runs great | Q8_0 | 4.91 GB | 54.3 t/s | 128k |
| Phi 4 Mini Instruct | 3.84B | Runs great | Q8_0 | 5.17 GB | 51.5 t/s | 32k |
| Granite 4.1 3B | 3.4B | Runs great | Q8_0 | 4.34 GB | 62 t/s | 64k |
| Qwen3 32B | 32.76B | Fits only at 2 to 3 bits | IQ2_XXS | 10.11 GB | 25.6 t/s | 8k |
| Qwen2.5 3B Instruct | 3.09B | Runs great | Q8_0 | 3.68 GB | 74 t/s | 32k |
| Phi 3 Mini 4k Instruct | 3.82B | Runs great | Q8_0 | 4.8 GB | 55.8 t/s | 4k |
| Llama 3.2 3B Instruct | 3.21B | Runs great | Q8_0 | 4.42 GB | 61 t/s | 64k |
| Granite 4.1 30B | 28.87B | Fits only at 2 to 3 bits | IQ2_XXS | 9.33 GB | 27.7 t/s | 8k |
| Qwen2.5 32B Instruct | 32.76B | Fits only at 2 to 3 bits | IQ2_XXS | 10.11 GB | 25.6 t/s | 8k |
| Qwen2.5 Coder 32B Instruct | 32.76B | Fits only at 2 to 3 bits | IQ2_XXS | 10.11 GB | 25.6 t/s | 8k |
| LFM2.5 2.6B | 2.7B | Runs great | Q8_0 | 3.14 GB | 88.4 t/s | 128k |
| Qwen3.5 2B | 2.27B | Runs great | Q8_0 | 2.7 GB | 104.8 t/s | 256k |
| Gemma 3 27B Instruct | 27.43B | Fits only at 2 to 3 bits | IQ2_XXS | 8.11 GB | 32.3 t/s | 32k |
| Gemma 2 2B Instruct | 2.61B | Runs great | Q8_0 | 3.54 GB | 77.4 t/s | 8k |
| Starcoder2 3B | 3.03B | Runs great | Q8_0 | 3.34 GB | 83.3 t/s | 16k |
| Qwen3 1.7B | 2.03B | Runs great | Q8_0 | 3.22 GB | 85.7 t/s | 32k |
| DeepSeek R1 Distill Qwen 1.5B | 1.78B | Runs great | Q8_0 | 2.07 GB | 141.3 t/s | 128k |
| Qwen2.5 1.5B Instruct | 1.54B | Runs great | Q8_0 | 2.06 GB | 141.9 t/s | 32k |
| SmolLM2 1.7B | 1.71B | Runs great | Q8_0 | 3.53 GB | 77.4 t/s | 8k |
| OLMo 2 0425 1B | 1.48B | Runs great | Q8_0 | 2.6 GB | 109.3 t/s | 4k |
| Llama 3.2 1B Instruct | 1.24B | Runs great | Q8_0 | 1.82 GB | 167.4 t/s | 128k |
| LFM2.5 1.2B Instruct | 1.17B | Runs great | Q8_0 | 1.46 GB | 221 t/s | 64k |
| Gemma 3 1B Instruct | 1B | Runs great | Q8_0 | 1.35 GB | 238 t/s | 32k |
| Qwen3.5 0.8B | 0.87B | Runs great | Q8_0 | 1.28 GB | 254.1 t/s | 256k |
| Qwen3 0.6B | 0.75B | Runs great | Q8_0 | 1.92 GB | 152.9 t/s | 32k |
| Qwen2.5 0.5B Instruct | 0.49B | Runs great | Q8_0 | 0.88 GB | 427.2 t/s | 32k |
| LFM2.5 350M | 0.35B | Runs great | Q8_0 | 0.68 GB | 661.6 t/s | 64k |
| GPT OSS 20B | 20.91B | Partial offload | n/a | 11.55 GB | 61.7 t/s | n/a |
| Qwen3.5 35B A3B | 35.95B | Partial offload | n/a | 20.48 GB | 53.9 t/s | n/a |
| GLM 4.7 Flash | 31.22B | Partial offload | n/a | 18.13 GB | 36.4 t/s | n/a |
| Gemma 4 26B A4B Instruct | 25.81B | Partial offload | n/a | 15.7 GB | 35.9 t/s | n/a |
| Qwen3 30B A3B | 30.53B | Partial offload | n/a | 18.08 GB | 35.1 t/s | n/a |
| Qwen3 Coder 30B A3B Instruct | 30.53B | Partial offload | n/a | 18.08 GB | 35.1 t/s | n/a |
| Qwen3 Coder Next | 79.67B | Partial offload | n/a | 45.22 GB | 32.6 t/s | n/a |
| Qwen3 Next 80B A3B Instruct | 81.32B | Partial offload | n/a | 46.15 GB | 32.1 t/s | n/a |
| GPT OSS 120B | 116.83B | Partial offload | n/a | 59.4 GB | 16.7 t/s | n/a |
| Phi 3.5 MoE Instruct | 41.87B | Partial offload | n/a | 24.87 GB | 12.9 t/s | n/a |
| Qwen3.5 122B A10B | 125.09B | Partial offload | n/a | 70.61 GB | 12.4 t/s | n/a |
| GLM 4.5 Air | 110.47B | Partial offload | n/a | 63.61 GB | 6.8 t/s | n/a |
| MiniMax M2.5 | 228.7B | Partial offload | n/a | 130.56 GB | 6.5 t/s | n/a |
| MiniMax M2.7 | 228.69B | Partial offload | n/a | 130.55 GB | 6.5 t/s | n/a |
| Mixtral 8x7B Instruct V0.1 | 46.7B | Partial offload | n/a | 27.59 GB | 6 t/s | n/a |
| MiniMax M3 | 427.04B | Partial offload | n/a | 240.84 GB | 3.4 t/s | n/a |
| Qwen3 235B A22B | 235.09B | Partial offload | n/a | 133.71 GB | 3.4 t/s | n/a |
| Gemma 4 31B Instruct | 31.27B | Partial offload | n/a | 21.31 GB | 2.6 t/s | n/a |
| Kimi K2.6 | 1026.88B | Partial offload | n/a | 577.78 GB | 2.2 t/s | n/a |
| Kimi K2 Instruct | 1026.41B | Partial offload | n/a | 577.52 GB | 2.2 t/s | n/a |
| DeepSeek V3.2 | 685.4B | Partial offload | n/a | 385.91 GB | 2.2 t/s | n/a |
| DeepSeek R1 | 684.53B | Partial offload | n/a | 385.42 GB | 2.2 t/s | n/a |
| GLM 5.1 | 753.86B | Partial offload | n/a | 424.51 GB | 2.1 t/s | n/a |
| GLM 4.5 | 358.34B | Partial offload | n/a | 204.37 GB | 1.4 t/s | n/a |
| GLM 5.2 | 753.33B | Partial offload | n/a | 424.21 GB | 1 t/s | n/a |
| Qwen2.5 VL 72B Instruct | 73.41B | Partial offload | n/a | 43.62 GB | 0.9 t/s | n/a |
| Qwen2.5 72B Instruct | 72.71B | Partial offload | n/a | 43.22 GB | 0.9 t/s | n/a |
| Llama 3.3 70B Instruct | 70.55B | Partial offload | n/a | 42.12 GB | 0.9 t/s | n/a |
| Llama 3 3 Nemotron Super 49B V1 | 49.87B | Partial offload | n/a | 47.99 GB | 0.8 t/s | n/a |
What it will not run
0 of the 86 architectures we track are out of reach here, even at 2-bit precision. Another 29 run by splitting the model between the card and system RAM, which works but drops generation to single digits for most dense models.
If you want more headroom
The next steps up in memory, in order. More memory changes which models load at all; more bandwidth changes how fast they answer, so the two are worth weighing separately.
| Device | Memory | Bandwidth | Models that fit | |
|---|---|---|---|---|
| GeForce RTX 5080 | 16 GB | 960 GB/s | 63 | Check price |
| GeForce RTX 5070 Ti | 16 GB | 896 GB/s | 63 | Check price |
| GeForce RTX 4080 SUPER | 16 GB | 736 GB/s | 63 | Check price |
| GeForce RTX 4080 | 16 GB | 717 GB/s | 63 | Check price |
Price links go to an Amazon search for the model name and are affiliate links: if you buy through one, we earn a commission at no cost to you. We do not take payment for placement, and the ordering above is by memory capacity alone.
Other devices with 12 GB
Same capacity, different speed. Once a model fits, bandwidth is what separates these.
Questions people ask about an RTX 2060 12GB
How much VRAM does a GeForce RTX 2060 12GB have?
The GeForce RTX 2060 12GB has 12 GB of VRAM, of which roughly 11.2 GB is available to a model once the operating system takes its share.
Can a GeForce RTX 2060 12GB run a 70B model?
Only partially. About 48 of 48 layers fit on the card and the rest run on the CPU, which brings generation down to roughly 32.6 tokens per second.
What is the best local model for a GeForce RTX 2060 12GB?
Gemma 4 12B Instruct at Q6_K is the strongest all-round fit: 10.84 GB used, around 23.7 tokens per second at 8k context.