Best local AI models for NVIDIA GTX 1060 5GB
5 GB GDDR5. At a 4k context, 85 of the 233 models in our catalog with verified parameter counts fit fully, up to Magicoder-S-DS 6.7B at 6.7B parameters.
Check your own machine against every model →The largest models that fit fully
The 30 largest of the 85 models that fit; every smaller model in the catalog fits too. Best quant means the highest quality compression whose weights and 4k context both sit inside the memory.
| Model | Parameters | Best quant that fits | Memory used at 4k |
|---|---|---|---|
| Magicoder-S-DS 6.7B | 6.7B | Q4_K_M | 4.9 GB |
| Phi-4-multimodal | 5.6B | Q5_K_M | 4.8 GB |
| Lumina-Next / Lumina-Image 2.0 | 5B | Q6_K | 4.9 GB |
| CogVideoX 2B / 5B | 5B | Q6_K | 4.9 GB |
| DeepSeek-VL2 | 4.5B | Q6_K | 4.4 GB |
| DeepFloyd IF | 4.3B | Q6_K | 4.2 GB |
| Phi-3.5-vision | 4.2B | Q6_K | 4.1 GB |
| Qwen3 4B | 4B | Q6_K | 3.9 GB |
| Gemma 3 4B | 4B | Q6_K | 3.9 GB |
| Gemma 4 E4B | 4B | Q6_K | 3.9 GB |
| MiniCPM 3 4B | 4B | Q6_K | 3.9 GB |
| Danube 3 4B | 4B | Q6_K | 3.9 GB |
| Fish Speech 1.5 / OpenAudio S1 | 4B | Q6_K | 3.9 GB |
| Phi-3 Mini | 3.8B | Q5_K_M | 4.8 GB |
| Phi-4-mini-instruct | 3.8B | Q8_0 | 4.8 GB |
| Phi-3.5 Mini | 3.8B | Q8_0 | 4.8 GB |
| OmniGen / OmniGen2 | 3.8B | Q8_0 | 4.8 GB |
| SD Cascade (Würstchen v3) | 3.6B | Q8_0 | 4.6 GB |
| SDXL Turbo | 3.5B | Q8_0 | 4.5 GB |
| SDXL Lightning | 3.5B | Q8_0 | 4.5 GB |
| ACE-Step | 3.5B | Q8_0 | 4.5 GB |
| Stable Diffusion XL | 3.417B | FP8 / optimized | 4.1 GB |
| MusicGen small/medium/large | 3.3B | Q8_0 | 4.2 GB |
| SmolLM3 3B | 3B | Q8_0 | 3.8 GB |
| Replit Code v1.5 3B | 3B | Q8_0 | 3.8 GB |
| Kandinsky 3.1 | 3B | Q8_0 | 3.8 GB |
| Voxtral Mini / Small | 3B | Q8_0 | 3.8 GB |
| Orpheus TTS | 3B | Q8_0 | 3.8 GB |
| Higgs Audio v2 | 3B | Q8_0 | 3.8 GB |
| Allegro | 2.8B | Q8_0 | 3.6 GB |
Close, but only with CPU offload
These need more than the card holds at their smallest practical quant, so part of the model runs from system memory (figures assume 32 GB of it). They work, several times slower.
| Model | Parameters | Memory at Q4_K_M | System RAM at 4k |
|---|---|---|---|
| Mistral 7B | 7B | 5.7 GB needed | 7.7 GB |
| Qwen2.5 0.5B / 1.5B / 3B / 7B | 7B | 5.1 GB needed | 7.1 GB |
| OLMo 2 1B / 7B | 7B | 5.1 GB needed | 7.1 GB |
| Falcon 3 1B / 3B / 7B | 7B | 5.1 GB needed | 7.1 GB |
| Command R7B | 7B | 5.1 GB needed | 7.1 GB |
| OpenHermes 2.5 | 7B | 5.1 GB needed | 7.1 GB |
| Zephyr 7B Beta | 7B | 5.1 GB needed | 7.1 GB |
| OpenChat 3.5 | 7B | 5.1 GB needed | 7.1 GB |
| Starling LM 7B | 7B | 5.1 GB needed | 7.1 GB |
| Codestral Mamba 7B | 7B | 5.1 GB needed | 7.1 GB |
How to read this
The NVIDIA GTX 1060 5GB graphics card features 5 GB of GDDR5 video memory. This onboard memory is the most critical factor for running local artificial intelligence models. To achieve acceptable generation speeds, the model weights must fit entirely within this video memory. If a model exceeds this limit, the system must transfer data to system memory, which slows down performance significantly.
Quantization is a compression method that reduces the memory footprint of these models. The quant column indicates the highest quality quantization level that fits within your video memory. For example, Magicoder-S-DS 6.7B fits at Q4_K_M quantization using 4.9 GB of video memory. Similarly, Phi-4-multimodal fits at Q5_K_M quantization using 4.8 GB of video memory. These compressed formats allow you to run larger models on your hardware.
Many capable models fit completely within the video memory limit. Lumina-Next and Lumina-Image 2.0 at Q6_K quantization use 4.9 GB of video memory. CogVideoX 2B and 5B also use 4.9 GB at Q6_K quantization. DeepSeek-VL2 fits at Q6_K using 4.4 GB of video memory. DeepFloyd IF uses 4.2 GB at Q6_K quantization. Phi-3.5-vision fits at Q6_K using 4.1 GB of video memory.
Several 4B models fit comfortably with Q6_K quantization using 3.9 GB of video memory. This group includes Qwen3 4B, Gemma 3 4B, Gemma 4 E4B, MiniCPM 3 4B, Danube 3 4B, Fish Speech 1.5, and OpenAudio S1. Models like Phi-3 Mini, Phi-4-mini-instruct, Phi-3.5 Mini, and OmniGen or OmniGen2 fit at various quants using 4.8 GB of video memory. Image and audio models like SD Cascade, SDXL Turbo, SDXL Lightning, ACE-Step, Stable Diffusion XL, and MusicGen small, medium, or large also fit within the video memory limit.
Smaller models fit with minimal compression. SmolLM3 3B, Replit Code v1.5 3B, Kandinsky 3.1, Voxtral Mini or Small, Orpheus TTS, and Higgs Audio v2 all fit at Q8_0 quantization using 3.8 GB of video memory. Allegro fits at Q8_0 quantization using 3.6 GB of video memory. These options leave a small buffer of video memory for your display output and operating system tasks.
When you run larger 7B models, you must use CPU offloading. This process splits the workload between your graphics card and your system RAM. For instance, Mistral 7B at Q4_K_M requires 5.7 GB of video memory and 7.7 GB of system RAM. Other 7B models like Qwen2.5, OLMo 2, Falcon 3, Command R7B, OpenHermes 2.5, Zephyr 7B Beta, OpenChat 3.5, Starling LM 7B, and Codestral Mamba 7B require 5.1 GB of video memory and 7.1 GB of system RAM at Q4_K_M. This offloading allows you to run the models but reduces generation speed.
You must also consider the context window size. The memory figures listed are calculated for a standard 4k context window. If you increase the context window to process longer documents or longer chat histories, the memory usage will rise. This extra memory demand can push a model over the 5 GB limit and trigger slow system RAM offloading.