Best local AI models for NVIDIA GTX TITAN BLACK

The largest models that fit fully

The 30 largest of the 114 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 GTX TITAN BLACK features 6 GB of GDDR5 graphics memory. This memory size determines which artificial intelligence models can run directly on your hardware. To run a model entirely on the graphics card, the model files and its active working memory must fit within this 6 GB limit. If a model exceeds this limit, it will fail to load or run extremely slowly.

The quantization column indicates the compression level used on the model weights. Quantization reduces the size of a model so it fits into smaller memory spaces. For this graphics card, the best quantization level for 8B models is Q4_K_M, which uses 5.9 GB of memory. Examples of these models include Granite 3.3 8B, Ministral 8B, InternLM 3 8B, OpenCoder 8B, Seed-Coder 8B, MiniCPM-V 2.6, MiniCPM-o 2.6, Idefics 3 8B, Fuyu-8B, Emu3, and Stable Diffusion 3.5 Large. The EXAONE 3.5 7.8B model also runs at Q4_K_M using 5.7 GB of memory.

Smaller models can run at a higher quality Q5_K_M quantization level. Mistral 7B uses 5.7 GB of memory at Q4_K_M, but many other 7B models can run at Q5_K_M using exactly 6 GB of memory. These include Qwen2.5 7B, OLMo 2 7B, Falcon 3 7B, Command R7B, OpenHermes 2.5, Zephyr 7B Beta, OpenChat 3.5, Starling LM 7B, Codestral Mamba 7B, CodeGemma 7B, aiXcoder-7B, Nxcode, CodeQwen 1.5 7B, Janus-Pro 7B, Ruyi-Mini-7B, Qwen2-Audio 7B, Qwen2.5-Omni 7B, and YuE. Magicoder-S-DS 6.7B runs at Q5_K_M using 5.7 GB of memory.

When a model is too large for the 6 GB graphics memory, you can offload parts of it to your system RAM. This offload process requires a system with 32 GB of system RAM. Offloading allows you to run larger models, but it costs performance because system RAM is much slower than graphics memory. For example, Llama 3.1 8B needs 6.4 GB at Q4_K_M and requires 8.4 GB of system RAM. Chroma needs 6.5 GB at Q4_K_M and requires 8.5 GB of system RAM. Gemma 2 9B needs 8 GB at Q4_K_M and requires 10 GB of system RAM.

Other models that require system RAM offloading include Nemotron Nano 9B, GLM-4 9B, GLM-4.5-Air, Yi-Coder 9B, GLM-4-9B-Chat, CodeGeeX4, GLM-4V-9B, and GLM-4.1V-Thinking. These models all need 6.6 GB at Q4_K_M and require 8.6 GB of system RAM. Mochi 1 needs 7.3 GB at Q4_K_M and requires 9.3 GB of system RAM. Open-Sora 2.0 needs 8.1 GB at Q4_K_M and requires 10.1 GB of system RAM.

Users must also consider the context limit when running these models. The memory numbers listed are calculated for a standard 4k context window. If you increase the context window to process longer documents or longer chat histories, the memory usage will increase. This extra memory usage might push a model past the 6 GB limit of your graphics card and force the system to use slower system RAM.