Best local AI models for NVIDIA T1000 8GB

The largest models that fit fully

The 30 largest of the 123 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.

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.

How to read this

The NVIDIA T1000 8GB graphics card features 8 GB of GDDR6 memory. This dedicated memory determines the size of the artificial intelligence models you can run locally. When running models on your local hardware, the entire model weights should ideally fit within this video memory to ensure fast processing speeds. If a model exceeds this limit, your system must use slower alternative pathways.

Quantization is a method that compresses model files to make them fit into smaller memory spaces. In our lists, the best quant column shows the optimal compression level for each model on this hardware. For example, the Q4_K_M and Q5_K_M quants represent medium levels of quantization. The Q6_K quant represents a higher quality compression that preserves more original model accuracy while still fitting within your 8 GB limit.

Several highly capable models fit entirely within the local video memory of your card. Mochi 1 at 10B fits using the Q4_K_M quant and takes up 7.3 GB of memory. Gemma 2 9B uses exactly 8 GB of memory at the Q4_K_M quant. Other models like Nemotron Nano 9B, GLM-4 9B, Yi-Coder 9B, GLM-4-9B-Chat, and GLM-4V-9B utilize 7.7 GB of memory with the Q5_K_M quant. Chroma is another option at 8.9B that uses 7.6 GB of memory at the Q5_K_M quant.

For models optimized with the Q6_K quant, Llama 3.1 8B uses 7.4 GB of memory. Granite 3.3 8B, Ministral 8B, InternLM 3 8B, OpenCoder 8B, Seed-Coder 8B, MiniCPM-V 2.6, Idefics 3 8B, Fuyu-8B, Emu3, and Stable Diffusion 3.5 Large all fit by using 7.9 GB of memory. EXAONE 3.5 7.8B fits well at 7.7 GB. Popular 7B models like Mistral 7B use 7.4 GB, while Qwen2.5 7B, OLMo 2 7B, Falcon 3 7B, Command R7B, OpenHermes 2.5, Zephyr 7B Beta, OpenChat 3.5, Starling LM 7B, and Codestral Mamba 7B all require 6.9 GB of memory.

When a model is too large for the graphics card, you can use CPU offloading if your computer has at least 32 GB of system RAM. Offloading shares the workload between your graphics card and system memory, but it reduces processing speed. For instance, FLUX.1 dev requires 14.4 GB of video memory at FP8 and needs 16.4 GB of system RAM. Models like Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, Pixtral 12B, FLUX.1 schnell, FLUX.1 Kontext dev, and FLUX.1 Krea dev require 8.8 GB of video memory at Q4_K_M and 10.8 GB of system RAM. Open-Sora 2.0 needs 8.1 GB of video memory and 10.1 GB of system RAM, while Vicuna 13B needs 9.5 GB of video memory and 11.5 GB of system RAM.

You must also consider the context window size when planning your memory usage. The memory figures listed here are calculated using a standard 4k context window. If you increase the context window to process longer documents or longer chat histories, the system will require significantly more memory. This extra demand can push a model that normally fits inside your 8 GB limit into system RAM offloading.