Best local AI models for AMD FirePro W5100

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 FirePro W5100 is a workstation graphics card equipped with 4 GB of GDDR5 memory. This onboard memory size dictates the maximum size of the AI models you can run entirely on the hardware. When a model fits completely within this 4 GB frame, it executes much faster because the GPU can access the weights directly. If a model exceeds this limit, you must use CPU offloading to handle the extra data.

The quantization column indicates the compression level applied to each model. Quantization reduces the precision of model weights to save memory space. For example, Q4_K_M represents a medium four bit quantization, while Q6_K and Q8_0 offer higher precision at the cost of larger file sizes. Choosing the best quantization allows you to run larger models like the 5B Lumina-Next or CogVideoX 2B / 5B at Q4_K_M using 3.7 GB of your onboard memory.

For models that exceed 4 GB, you can offload layers to your system RAM. This setup assumes you have 32 GB of system RAM available. For instance, running Mistral 7B at Q4_K_M requires 5.7 GB of total memory, which means utilizing 7.7 GB of system RAM to assist the GPU. Other models like Qwen2.5 7B, OLMo 2 7B, Falcon 3 7B, Command R7B, and OpenHermes 2.5 require 5.1 GB of memory and use 7.1 GB of system RAM under this offload method.

Offloading allows you to run capable models like the 6.7B Magicoder-S-DS or the 5.6B Phi-4-multimodal, but it comes with a performance cost. Transferring data between the GPU and system RAM over the system bus is much slower than using the onboard GDDR5 memory. This transfer bottleneck reduces the generation speed significantly compared to running smaller models fully on the card.

You must also consider the memory cost of context length. The listed memory figures represent the base model size. Running these models with a standard 4k context window requires additional memory to store the active conversation history. For models that already sit close to the limit, such as Qwen3 4B using 3.9 GB at Q6_K, processing long context windows may exceed the 4 GB boundary and trigger automatic offloading.