Best local AI models for AMD RX 6450M

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 6450M is an entry level laptop graphics card equipped with 4 GB of GDDR6 memory. This dedicated memory size determines which artificial intelligence models can run entirely on your hardware. For local execution, the model weights and the context data must fit within this 4 GB limit to maintain fast processing speeds. If a model exceeds this capacity, the system must transfer data between your graphics card and system memory, which slows down performance.

To fit larger models into the 4 GB limit, developers use quantization. The quant column shows the compression level applied to the model weights. For example, a Q4_K_M quant uses approximately four bits per weight, while Q6_K and Q8_0 quants offer higher precision at the cost of larger file sizes. On this hardware, 5B models like Lumina-Next or CogVideoX 2B require a Q4_K_M quant to fit within 3.7 GB of memory. Smaller 4B models like Qwen3 4B, Gemma 3 4B, and MiniCPM 3 4B can run at a higher quality Q6_K quant using 3.9 GB.

When selecting a model, you must also consider the context window. The memory figures listed for these models assume a basic 4k context window. As your conversation grows longer, the active memory required to track the history increases. Running a model close to the 4 GB limit, such as DeepSeek-VL2 at 3.8 GB or Qwen3 4B at 3.9 GB, leaves very little room for extended context. If your prompts are long, you may need to select smaller models like Stable Diffusion 3.5 Medium at 3.2 GB to avoid running out of memory.

If you want to run larger models, you can use CPU offloading. This technique splits the workload between your graphics card and your system RAM. Assuming your laptop has 32 GB of system RAM, you can run 7B models like Mistral 7B, Qwen2.5 7B, or Falcon 3 7B. Offloading allows you to run these models, but the transfer of data between the system RAM and the graphics card reduces the generation speed significantly.

For offloaded models, the memory requirements are split. Mistral 7B requires 5.7 GB at Q4_K_M and needs 7.7 GB of system RAM. Similarly, Qwen2.5 7B, Falcon 3 7B, and Command R7B require 5.1 GB at Q4_K_M and need 7.1 GB of system RAM. This approach lets you access superior reasoning capabilities on your AMD RX 6450M, provided you are willing to accept slower response times.