Best local AI models for AMD Pro 5500 XT

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 AMD Radeon Pro 5500 XT graphics card features 8 GB of GDDR6 dedicated video memory. This memory pool determines the size of the artificial intelligence models you can run locally. To run a model entirely on the graphics hardware, the model files and the active working memory must fit within this 8 GB limit. Keeping the entire model on the card ensures the fastest processing speeds.

The quantization column indicates the compression level applied to each model. Quantization reduces the precision of model weights to save space. For example, a Q4_K_M quantization represents a medium four bit compression. A Q6_K quantization represents a six bit compression which preserves more original quality but requires more memory. The Mochi 1 10B model fits at a Q4_K_M quantization using 7.3 GB of video memory. Gemma 2 9B fits at Q4_K_M using exactly 8 GB of video memory.

Several 9B models fit well on this hardware using Q5_K_M quantization. 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 each use 7.7 GB of video memory. Chroma is an 8.9B model that uses 7.6 GB of video memory at Q5_K_M. Llama 3.1 8B fits at Q5_K_M using 7.4 GB of video memory.

Models of 8B size can run at a higher Q6_K quantization. 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 use 7.9 GB of video memory. EXAONE 3.5 7.8B uses 7.7 GB of video memory at Q6_K. Mistral 7B uses 7.4 GB of video memory at Q6_K. 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 use 6.9 GB of video memory at Q6_K.

When a model exceeds the 8 GB video memory limit you can offload parts of the model to your system RAM. This offloading requires a host system with at least 32 GB of system RAM. Offloading allows you to run larger models but it reduces processing speed because system RAM is slower than video memory. For example, Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, Pixtral 12B, FLUX.1 schnell, FLUX.1 Kontext dev, and FLUX.1 Krea dev need 8.8 GB at Q4_K_M and require 10.8 GB of system RAM. FLUX.1 dev needs 14.4 GB at FP8 and requires 16.4 GB of system RAM. Vicuna 13B needs 9.5 GB at Q4_K_M and requires 11.5 GB of system RAM.

All memory calculations assume a standard 4k context window. The context window is the amount of text the model can read and write at one time. If you increase the context window beyond 4k tokens the model will require significantly more video memory. This extra memory usage may force you to use a lower quantization or offload the model to system RAM.