Best local AI models for NVIDIA T600 Laptop
4 GB GDDR6. 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 T600 Laptop graphics card features 4 GB of GDDR6 memory. This dedicated video memory determines the size of the artificial intelligence models you can run locally. To load a model entirely on your graphics processor, the model files and the active memory space must fit within this 4 GB limit. Running models locally ensures your data remains private and does not require an active internet connection.
The quantization column indicates the compression level used to shrink the model files. Uncompressed models are too large for this hardware. Quantization formats like Q4_K_M, Q5_K_M, Q6_K, and Q8_0 reduce the precision of the model weights. This process lowers the memory footprint so the models can fit into your video memory. A higher quantization number like Q8_0 preserves more original quality but requires more space than Q4_K_M.
Several capable models fit completely within the 4 GB limit of your hardware. The largest options include Lumina-Next or Lumina-Image 2.0 at 5B using the Q4_K_M quant which consumes 3.7 GB of memory. You can also run CogVideoX 2B or 5B at the 5B size using Q4_K_M for 3.7 GB of memory. Vision models like DeepSeek-VL2 at 4.5B fit with the Q5_K_M quant using 3.8 GB of memory. Phi-3.5-vision at 4.2B fits using the Q5_K_M quant and consumes 3.6 GB of memory.
For text generation and audio tasks, you can run Qwen3 4B, Gemma 3 4B, Gemma 4 E4B, MiniCPM 3 4B, Danube 3 4B, or Fish Speech 1.5 or OpenAudio S1. These 4B models use the Q6_K quant and require 3.9 GB of memory. The Phi-4-mini-instruct and Phi-3.5 Mini models at 3.8B use the Q6_K quant and require 3.7 GB of memory. Image generation models like SDXL Turbo and SDXL Lightning at 3.5B fit using the Q6_K quant and consume 3.4 GB of memory.
When a model exceeds the 4 GB video memory limit, you must use CPU offload. This technique splits the model between your graphics card and your system RAM. We assume your system has 32 GB of system RAM for these setups. For example, Mistral 7B needs 5.7 GB of memory at the Q4_K_M quant, which requires 7.7 GB of system RAM. Qwen2.5 0.5B or 1.5B or 3B or 7B at the 7B size needs 5.1 GB of memory at Q4_K_M, requiring 7.1 GB of system RAM. Offloading allows you to run larger models but reduces processing speed.
You must also consider the active context window when calculating memory usage. The memory figures listed here assume a standard 4k context window. If you increase the context window to process longer documents or chat histories, the memory usage will rise. This extra memory demand can exceed your 4 GB limit and force the system to slow down or fail to generate responses.