Best local AI models for NVIDIA RTX 5880 Ada Generation

The largest models that fit fully

The 30 largest of the 186 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.

How to read this

The NVIDIA RTX 5880 Ada Generation workstation graphics card features 48 GB of GDDR6 memory. This large frame buffer determines the maximum size of the artificial intelligence models you can run locally. To run a model entirely on the graphics card, the model weights and the active context memory must fit within this 48 GB limit.

The quantization column indicates the compression level applied to each model. Quantization reduces the precision of model weights to save memory. A Q8_0 quant represents eight bit quantization, which preserves high output quality while reducing the footprint. For larger models, a Q5_K_M or Q6_K quant is used to fit the model into the 48 GB frame buffer.

For the largest models, Jamba 1.5 Mini or Large at 52B fits using a Q5_K_M quant which uses 44.3 GB of memory. Llama 3.1 Nemotron 51B also fits at Q5_K_M using 43.5 GB. Mixtral 8x7B at 47B fits at Q6_K using 46.2 GB. These configurations maximize the available memory on your hardware.

Models like Seed-OSS 36B, Command R 35B, and Qwen3.6-35B-A3B run at the Q8_0 quant level using 45.8 GB and 44.5 GB respectively. You can also run Yi 1.5 34B, Granite Code 34B, LLaVA 1.5 or 1.6 34B, and Ovis 2 at Q8_0 using 43.2 GB. DeepSeek-Coder 33B and WizardCoder 33B fit at Q8_0 using 42 GB.

Medium sized models fit comfortably at full Q8_0 quantization. This includes OTel 2.0 LLM 31B IT using 44.9 GB, and Qwen3 32B, Qwen3.5 dense variants, Aya Expanse 32B, Granite 4.0 Small, and Qwen2.5-Coder 32B using 40.7 GB. Qwen3-30B-A3B and Qwen3-Coder 30B-A3B use 38.2 GB. Gemma 3 27B and Wan 2.2 or 2.5 use 34.3 GB. Gemma 4 26B-A4B uses 33.1 GB, Aria uses 31.8 GB, Mistral Small 3.2 uses 30.5 GB, and Solar Pro uses 28 GB.

All listed configurations assume a standard four kilobyte context window. Increasing the context window length requires additional graphics memory for the key value cache. If you increase the context length, you may need to select a lower quantization level to prevent the model from exceeding the 48 GB limit.

Running models entirely on the graphics card ensures the fastest processing speeds. This system configuration does not require CPU offloading to system RAM. Keeping all model weights inside the fast GDDR6 memory avoids the severe speed penalties associated with transferring data across the system bus.