Best local AI models for NVIDIA GTX 1050 Ti 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 Ti MAX-Q is an entry level laptop graphics card with 4 GB of GDDR5 memory. This physical memory limit determines which local AI models you can run entirely on your graphics hardware. To run a model without slowdowns, its active weights and working memory must fit inside this 4 GB limit. If a model exceeds this capacity, your system must use slower system memory.
Quantization is a compression method that reduces model size. The quant column shows the best format that fits your hardware. For example, a Q6_K quant uses six bits per weight to preserve high accuracy. A Q8_0 quant uses eight bits for even better quality but requires more space. Lower quants like Q4_K_M or Q5_K_M compress models further so larger architectures can fit into your 4 GB boundary.
Several capable models fit completely within your graphics memory. The largest options are Lumina-Next or Lumina-Image 2.0 and CogVideoX 2B or 5B at 5B parameters using a Q4_K_M quant which uses 3.7 GB. DeepSeek-VL2 at 4.5B parameters fits at Q5_K_M using 3.8 GB. You can also run Phi-3.5-vision at 4.2B parameters using 3.6 GB. For text generation, Qwen3 4B, Gemma 3 4B, Gemma 4 E4B, MiniCPM 3 4B, Danube 3 4B, and Fish Speech 1.5 or OpenAudio S1 all fit at Q6_K using 3.9 GB.
Other fully local options include Phi-4-mini-instruct, Phi-3.5 Mini, and OmniGen or OmniGen2 at 3.8B parameters using 3.7 GB at Q6_K. Image generation models like SD Cascade (Würstchen v3) at 3.6B parameters use 3.5 GB. SDXL Turbo, SDXL Lightning, and ACE-Step at 3.5B parameters use 3.4 GB. MusicGen small/medium/large at 3.3B parameters uses 3.2 GB. Highly accurate Q8_0 options include SmolLM3 3B, Replit Code v1.5 3B, Kandinsky 3.1, Voxtral Mini or Small, Orpheus TTS, and Higgs Audio v2 which all use 3.8 GB.
When a model is too large for your 4 GB of VRAM, you can offload parts of it to your system RAM. This process requires a system with 32 GB of system RAM. Offloading allows you to run larger models but reduces processing speed. For instance, Mistral 7B needs 5.7 GB of VRAM at Q4_K_M and 7.7 GB of system RAM. Qwen2.5 0.5B / 1.5B / 3B / 7B, OLMo 2 1B / 7B, Falcon 3 1B / 3B / 7B, Command R7B, and OpenHermes 2.5 all need 5.1 GB of VRAM at Q4_K_M and 7.1 GB of system RAM.
Other offload options include Phi-4-multimodal at 5.6B parameters 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. Stable Diffusion XL at 3.417B parameters needs 4.1 GB at FP8 or optimized settings along with 6.1 GB of system RAM. Phi-3 Mini at 3.8B parameters needs 4.4 GB at Q4_K_M and 6.4 GB of system RAM.
You must monitor your context window size when running these models. The listed memory usage figures assume a standard 4k context window. If you increase the context length to process longer documents, the memory usage will rise. This extra memory demand can push a model past your 4 GB limit and trigger slow system RAM usage.