Best local AI models for NVIDIA T1000
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 T1000 features 4 GB of GDDR6 memory. This memory stores both the model weights and the context window. Keeping the total usage under 4 GB is necessary for local execution. Exceeding this limit causes the system to move data to slower system RAM. This process significantly reduces performance.
The quant column refers to quantization. Quantization reduces the precision of model weights to save space. A lower precision allows larger models to fit into the 4 GB memory limit. For example, Q4_K_M is a common choice for larger models like Lumina Next or CogVideoX 2B. Q6_K and Q8_0 provide higher precision for smaller models like Qwen3 4B or SmolLM3 3B.
Several models fit entirely within the 4 GB limit. Lumina Next and CogVideoX 5B use 3.7 GB at Q4_K_M. DeepSeek VL2 uses 3.8 GB at Q5_K_M. Qwen3 4B, Gemma 3 4B, and MiniCPM 3 4B use 3.9 GB at Q6_K. Phi 4 mini instruct and OmniGen use 3.7 GB at Q6_K. Stable Diffusion 3.5 Medium uses 3.2 GB at Q8_0.
Some models exceed the 4 GB limit and require CPU offloading. This method uses system RAM to hold parts of the model that do not fit on the GPU. Stable Diffusion XL requires 4.1 GB at FP8 and 6.1 GB of system RAM. Mistral 7B requires 5.7 GB at Q4_K_M and 7.7 GB of system RAM. Qwen2.5 7B, OLMo 2 7B, and Falcon 3 7B require 5.1 GB at Q4_K_M and 7.1 GB of system RAM.
Memory usage figures assume a standard context window. Increasing the context length beyond 4k tokens requires more memory. This extra memory usage can force a model to exceed the 4 GB limit. Users should monitor memory usage when increasing context length. High context usage may lead to slower inference speeds even if the model fits initially.
Always verify the specific quant version before loading a model. The listed memory usage is specific to the provided quant levels. Using a higher precision quant than listed will likely exceed the 4 GB capacity of the NVIDIA T1000. Use the provided figures as a guide for selecting compatible models for your hardware.