Best local AI models for NVIDIA GTX 1050 MAX-Q
4 GB GDDR5. At a 4k context, 81 of the 233 models in our catalog with verified parameter counts fit fully, up to Lumina-Next / Lumina-Image 2.0 at 5B parameters.
Check your own machine against every model →The largest models that fit fully
The 30 largest of the 81 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 |
|---|---|---|---|
| Lumina-Next / Lumina-Image 2.0 | 5B | Q4_K_M | 3.7 GB |
| CogVideoX 2B / 5B | 5B | Q4_K_M | 3.7 GB |
| DeepSeek-VL2 | 4.5B | Q5_K_M | 3.8 GB |
| DeepFloyd IF | 4.3B | Q5_K_M | 3.7 GB |
| Phi-3.5-vision | 4.2B | Q5_K_M | 3.6 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-4-mini-instruct | 3.8B | Q6_K | 3.7 GB |
| Phi-3.5 Mini | 3.8B | Q6_K | 3.7 GB |
| OmniGen / OmniGen2 | 3.8B | Q6_K | 3.7 GB |
| SD Cascade (Würstchen v3) | 3.6B | Q6_K | 3.5 GB |
| SDXL Turbo | 3.5B | Q6_K | 3.4 GB |
| SDXL Lightning | 3.5B | Q6_K | 3.4 GB |
| ACE-Step | 3.5B | Q6_K | 3.4 GB |
| MusicGen small/medium/large | 3.3B | Q6_K | 3.2 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 |
| Open-Sora Plan | 2.7B | Q8_0 | 3.4 GB |
| LFM2 1.2B / 2.6B | 2.6B | Q8_0 | 3.3 GB |
| Playground v2.5 | 2.6B | Q8_0 | 3.3 GB |
| Stable Diffusion 3.5 Medium | 2.5B | Q8_0 | 3.2 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 FP8 / optimized | System RAM at 4k |
|---|---|---|---|
| Stable Diffusion XL | 3.417B | 4.1 GB needed | 6.1 GB |
| Phi-3 Mini | 3.8B | 4.4 GB needed | 6.4 GB |
| Phi-4-multimodal | 5.6B | 4.1 GB needed | 6.1 GB |
| Magicoder-S-DS 6.7B | 6.7B | 4.9 GB needed | 6.9 GB |
| 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 |
How to read this
The NVIDIA GTX 1050 MAX-Q is an entry level laptop graphics card with 4 GB of GDDR5 memory. This dedicated memory size determines which local AI models you can run entirely on your hardware. To fit within this limit, models must use quantization. Quantization is a compression method that reduces model size while keeping most of the original intelligence. The quant column shows the best balance of quality and size for your card.
For models that fit completely inside your 4 GB of video memory, you can run text, vision, and image generation tasks. The largest fitting models include Lumina-Next or Lumina-Image 2.0 at 5B using the Q4_K_M quant which takes 3.7 GB of memory. CogVideoX 2B or 5B also fits at 5B using Q4_K_M with 3.7 GB used. DeepSeek-VL2 at 4.5B fits using the Q5_K_M quant with 3.8 GB used. DeepFloyd IF at 4.3B fits using Q5_K_M with 3.7 GB used. Phi-3.5-vision at 4.2B fits using Q5_K_M with 3.6 GB used.
Several 4B models fit well using the Q6_K quant which uses 3.9 GB of memory. These include Qwen3 4B, Gemma 3 4B, Gemma 4 E4B, MiniCPM 3 4B, Danube 3 4B, and Fish Speech 1.5 or OpenAudio S1. You can also run Phi-4-mini-instruct, Phi-3.5 Mini, and OmniGen or OmniGen2 at 3.8B using the Q6_K quant with 3.7 GB used. For image generation, SD Cascade (Würstchen v3) at 3.6B uses 3.5 GB, while SDXL Turbo and SDXL Lightning at 3.5B use 3.4 GB under the Q6_K quant. ACE-Step at 3.5B uses 3.4 GB, and MusicGen small/medium/large at 3.3B uses 3.2 GB under Q6_K.
Highly compressed 3B models can run at the Q8_0 quant level using 3.8 GB of video memory. This group includes SmolLM3 3B, Replit Code v1.5 3B, Kandinsky 3.1, Voxtral Mini or Small, Orpheus TTS, and Higgs Audio v2. Slightly smaller models like Allegro at 2.8B use 3.6 GB under Q8_0. Open-Sora Plan at 2.7B uses 3.4 GB. LFM2 1.2B or 2.6B and Playground v2.5 at 2.6B use 3.3 GB. Stable Diffusion 3.5 Medium at 2.5B uses 3.2 GB under the Q8_0 quant.
When a model is too large for your 4 GB video memory, you can use CPU offload if you have 32 GB of system RAM. Offload means the system splits the model between your graphics card and your slower system memory. This process allows you to run larger models but it reduces your generation speed. For example, Stable Diffusion XL at 3.417B needs 4.1 GB at FP8 or optimized settings and requires 6.1 GB of system RAM. Phi-3 Mini at 3.8B needs 4.4 GB at Q4_K_M and requires 6.4 GB of system RAM.
Other offload options include Phi-4-multimodal at 5.6B which needs 4.1 GB at Q4_K_M and 6.1 GB of system RAM. Magicoder-S-DS 6.7B needs 4.9 GB at Q4_K_M and 6.9 GB of system RAM. Popular 7B models like Mistral 7B need 5.7 GB at Q4_K_M and 7.7 GB of system RAM. Qwen2.5 0.5B or 1.5B or 3B or 7B, OLMo 2 1B or 7B, Falcon 3 1B or 3B or 7B, Command R7B, and OpenHermes 2.5 all need 5.1 GB at Q4_K_M and 7.1 GB of system RAM.
Keep in mind that memory usage calculations assume a standard 4k context window. If you increase the context window to process longer documents or chat histories, the memory requirement will grow. Running close to your 4 GB limit with a large context can cause out of memory errors or force your system into slow CPU offloading.