Best local AI models for NVIDIA RTX 5000 Ada Generation

The largest models that fit fully

The 30 largest of the 183 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 RTX 5000 Ada Generation workstation graphics card features 32 GB of GDDR6 memory. This dedicated video memory determines the size of the artificial intelligence models you can run entirely on the hardware. Keeping the model files inside the graphics memory ensures fast processing speeds for text generation and reasoning tasks.

The quantization level represents the compression format of the model weights. Choosing the best quantization allows you to balance model accuracy and memory usage. For example, the Seed-OSS 36B model fits within 30.7 GB of memory using the Q5_K_M quantization. Similarly, Qwen3.6-35B-A3B and Command R (35B) both utilize 29.8 GB of memory at the Q5_K_M quantization level.

Several high performance models fit within the 32 GB limit at the Q5_K_M quantization. Yi 1.5 9B / 34B, Granite Code 3B to 34B, LLaVA 1.5 / 1.6 (7B to 34B), and Ovis 2 all require 29 GB of memory. DeepSeek-Coder 1.3B / 6.7B / 33B and WizardCoder 33B use 28.1 GB of memory. OTel 2.0 LLM 31B IT fits closely to the limit at 31.4 GB of memory.

You can run other models at higher quality levels like Q6_K or Q8_0. Qwen3 8B / 14B / 32B, Qwen3.5 (dense variants), Aya Expanse 8B / 32B, Granite 4.0 Small/Tiny, and Qwen2.5-Coder 0.5B to 32B use 31.5 GB of memory at Q6_K. Gemma 3 27B, Gemma 3 4B/12B/27B (vision), and Wan 2.2 / 2.5 use 26.6 GB of memory. Aria fits at Q8_0 using 31.8 GB of memory.

When a model exceeds the 32 GB graphics memory, you must offload parts of the model to your system RAM. This offloading process slows down the processing speed significantly. For instance, Mixtral 8x7B needs 34.4 GB at Q4_K_M and requires 36.4 GB of system RAM. Llama 3.1 Nemotron 51B needs 37.3 GB at Q4_K_M, and Jamba 1.5 Mini / Large needs 38.1 GB at Q4_K_M.

All memory calculations assume a standard 4k context window. If you increase the context window to process longer documents, the system will require more memory for the active session. You must leave some memory free on your graphics card to prevent performance slowdowns during long conversations.