Best local AI models for AMD RX 7600S

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 RX 7600S graphics card features 8 GB of GDDR6 dedicated memory. This hardware limit determines which artificial intelligence models you can run entirely on your GPU. Keeping the model parameters and active working memory under 8 GB ensures fast processing speeds. If a model exceeds this capacity, your system must transfer data to system memory, which slows down performance.

The quantization column indicates the compression level applied to each model. Quantization reduces the precision of model weights to save space. For example, Gemma 2 9B fits your card at the Q4_K_M quantization level, which uses exactly 8 GB of memory. Other models like Llama 3.1 8B run at the Q5_K_M quantization level using 7.4 GB. Smaller models like Mistral 7B can run at the higher quality Q6_K quantization level while using only 7.4 GB.

Running models with context windows set to 4k tokens requires extra memory. The listed memory usage figures represent the base requirements for loading the models. When you generate longer conversations or process large documents, the context memory consumption increases. You must leave a small buffer of free VRAM on your AMD RX 7600S to prevent out of memory errors during long generation tasks.

When a model is too large for the 8 GB graphics memory, you can use CPU offload. This technique shares the workload between your GPU and your system RAM. We assume a standard system configuration with 32 GB of system RAM for these scenarios. Offloading allows you to run larger architectures, but the transfer of data between the system RAM and the GPU bottleneck reduces the generation speed.

Several larger models are accessible through CPU offloading. For instance, Mistral NeMo 12B, Gemma 3 12B, and Gemma 4 12B require 8.8 GB of memory at the Q4_K_M quantization level, which uses 10.8 GB of system RAM. The FLUX.1 dev model requires 14.4 GB at FP8 or optimized settings, which utilizes 16.4 GB of system RAM. Vicuna 13B needs 9.5 GB at Q4_K_M quantization and uses 11.5 GB of system RAM.