Best local AI models for NVIDIA GTX 1050 Ti Laptop
2 GB GDDR5. At a 4k context, 56 of the 233 models in our catalog with verified parameter counts fit fully, up to Allegro at 2.8B parameters.
Check your own machine against every model →The largest models that fit fully
The 30 largest of the 56 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 |
|---|---|---|---|
| Allegro | 2.8B | Q4_K_M | 2 GB |
| Open-Sora Plan | 2.7B | Q4_K_M | 2 GB |
| LFM2 1.2B / 2.6B | 2.6B | Q4_K_M | 1.9 GB |
| Playground v2.5 | 2.6B | Q4_K_M | 1.9 GB |
| Stable Diffusion 3.5 Medium | 2.5B | Q4_K_M | 1.8 GB |
| Canary 1B / Qwen-2.5B | 2.5B | Q4_K_M | 1.8 GB |
| SeamlessM4T v2 | 2.3B | Q5_K_M | 2 GB |
| Parler-TTS | 2.2B | Q5_K_M | 1.9 GB |
| Kimi K3 DSpark | 2.2B | Q5_K_M | 2 GB |
| SmolVLM 256M / 500M / 2B | 2B | Q6_K | 2 GB |
| Stable Diffusion 3 Medium | 2B | Q6_K | 2 GB |
| Pyramid Flow | 2B | Q6_K | 2 GB |
| Wav2Vec2 / XLS-R | 2B | Q6_K | 2 GB |
| Moondream 2 | 1.9B | Q6_K | 1.9 GB |
| Qwen3 1.7B | 1.7B | Q6_K | 1.7 GB |
| SmolLM2 135M / 360M / 1.7B | 1.7B | Q6_K | 1.7 GB |
| StableLM 2 1.6B | 1.6B | Q8_0 | 2 GB |
| Sana 0.6B / 1.6B | 1.6B | Q8_0 | 2 GB |
| Zonos 0.1 | 1.6B | Q8_0 | 2 GB |
| Dia 1.6B | 1.6B | Q8_0 | 2 GB |
| Whisper Large v3 | 1.55B | Q8_0 | 2 GB |
| ControlNet / T2I-Adapter / IP-Adapter | 1.5B | Q8_0 | 1.9 GB |
| Hunyuan-DiT | 1.5B | Q8_0 | 1.9 GB |
| Stable Video Diffusion | 1.5B | Q8_0 | 1.9 GB |
| Whisper Large v2 / turbo | 1.5B | Q8_0 | 1.9 GB |
| AudioGen | 1.5B | Q8_0 | 1.9 GB |
| AudioLDM 2 | 1.5B | Q8_0 | 1.9 GB |
| Tango 2 | 1.4B | Q8_0 | 1.8 GB |
| TinyLlama 1.1B | 1.1B | Q8_0 | 1.4 GB |
| SantaCoder 1.1B | 1.1B | Q8_0 | 1.4 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 |
|---|---|---|---|
| SmolLM3 3B | 3B | 2.2 GB needed | 4.2 GB |
| Replit Code v1.5 3B | 3B | 2.2 GB needed | 4.2 GB |
| Kandinsky 3.1 | 3B | 2.2 GB needed | 4.2 GB |
| Voxtral Mini / Small | 3B | 2.2 GB needed | 4.2 GB |
| Orpheus TTS | 3B | 2.2 GB needed | 4.2 GB |
| Higgs Audio v2 | 3B | 2.2 GB needed | 4.2 GB |
| MusicGen small/medium/large | 3.3B | 2.4 GB needed | 4.4 GB |
| Stable Diffusion XL | 3.417B | 4.1 GB needed | 6.1 GB |
| SDXL Turbo | 3.5B | 2.6 GB needed | 4.6 GB |
| SDXL Lightning | 3.5B | 2.6 GB needed | 4.6 GB |
How to read this
The NVIDIA GTX 1050 Ti Laptop graphics card features 2 GB of GDDR5 memory. This dedicated video memory is the main limit for running local artificial intelligence models. To run a model entirely on your graphics hardware, the model files and active memory must fit inside this 2 GB limit. If a model requires more memory than your card has, the system will slow down or fail to run the model.
The quantization column shows the compression level used to shrink these models. Quantization reduces the precision of model weights to save space. A Q4_K_M quant represents a four bit compression that keeps good accuracy while using much less memory. Higher quants like Q6_K or Q8_0 offer better quality but require more space. For example, the Allegro 2.8B model fits in 2 GB of memory using a Q4_K_M quant. The TinyLlama 1.1B model fits in 1.4 GB of memory using a Q8_0 quant.
You can run larger models by offloading parts of the workload to your system RAM. This setup assumes your laptop has 32 GB of system RAM. Offloading allows you to run models like the 3B SmolLM3 or the 3.5B SDXL Turbo. The SDXL Turbo model needs 2.6 GB of video memory at Q4_K_M and 4.6 GB of system RAM. Offloading comes with a speed cost. Moving data between your system RAM and your graphics card is much slower than running everything on the GDDR5 memory.
Context window size also affects your memory usage. Running a model with a standard four thousand token context window requires extra memory. This extra memory is used to store the active conversation history. If you use the maximum context window, you may need to choose smaller models or lower quantization levels to avoid running out of your 2 GB video memory.
Many small models perform well on this hardware. The Qwen3 1.7B model fits in 1.7 GB of memory using a Q6_K quant. The Whisper Large v3 model fits in 2 GB of memory using a Q8_0 quant. These options let you run text generation, speech recognition, and image creation locally on your laptop.