Best local AI models for AMD RX 6500 XT

The largest models that fit fully

The 30 largest of the 81 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 RX 6500 XT graphics card features 4 GB of GDDR6 video memory. This hardware limit dictates which local AI models can run directly on your GPU. To execute models without system slowdowns, the model weights and active context must fit entirely within this 4 GB VRAM boundary. When a model exceeds this capacity, the system must offload data to your system RAM, which reduces processing speed.

Quantization is a compression method that reduces the memory footprint of AI models. The quant column shows the best balance of size and quality for this GPU. For example, Q4_K_M represents a four bit quantization, while Q6_K and Q8_0 represent six bit and eight bit formats. Higher quant levels preserve more model intelligence but require more memory. Lower quant levels allow larger models to fit into the 4 GB VRAM space.

Several highly capable models fit completely inside the 4 GB VRAM limit. Lumina-Next and CogVideoX 2B / 5B can run at the 5B parameter size using the Q4_K_M quant, consuming 3.7 GB of memory. DeepSeek-VL2 runs at 4.5B parameters using Q5_K_M quant with 3.8 GB used. For text and vision tasks, Phi-3.5-vision fits at 4.2B parameters using Q5_K_M quant, requiring 3.6 GB of VRAM. Qwen3 4B, Gemma 3 4B, Gemma 4 E4B, MiniCPM 3 4B, and Danube 3 4B all run at the Q6_K quant level, utilizing 3.9 GB of memory.

Audio and image generation models also fit within the local VRAM. Fish Speech 1.5 / OpenAudio S1 uses 3.9 GB at Q6_K quant. SDXL Turbo and SDXL Lightning run at 3.5B parameters with Q6_K quant, using 3.4 GB of VRAM. MusicGen small/medium/large fits at 3.3B parameters using Q6_K quant, consuming 3.2 GB. For higher precision, SmolLM3 3B and Kandinsky 3.1 run at the Q8_0 quant level, using 3.8 GB of VRAM. Stable Diffusion 3.5 Medium fits at 2.5B parameters using Q8_0 quant, requiring 3.2 GB of memory.

When a model is too large for 4 GB of VRAM, you can offload layers to your system RAM. This process assumes you have 32 GB of system RAM. For instance, Mistral 7B requires 5.7 GB at Q4_K_M quant and needs 7.7 GB of system RAM. Qwen2.5 0.5B / 1.5B / 3B / 7B at the 7B size requires 5.1 GB at Q4_K_M quant and needs 7.1 GB of system RAM. Offloading allows you to run these larger models, but the transfer of data between VRAM and system RAM will slow down generation speeds significantly.

Users must also consider the 4k context caveat when running models near the VRAM limit. The memory numbers listed represent the model weights alone. Running text models with long conversations or large prompts increases memory usage. If you generate long responses or use a 4k context window, the active memory will exceed the 4 GB limit, causing the system to offload to system RAM and slow down performance.