Best local AI models for NVIDIA RTX 5050

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 NVIDIA RTX 5050 graphics card comes equipped with 8 GB of GDDR6 video memory. This dedicated memory size is the most critical factor when running artificial intelligence models locally. To achieve fast processing speeds, the entire active portion of a model should fit directly inside this video memory. If a model exceeds this limit, your system must transfer data back and forth from system memory, which slows down performance.

The quantization column indicates the compression level used to shrink these models. Raw models are often too large for consumer hardware, so they are quantized to lower precision formats like Q4_K_M, Q5_K_M, or Q6_K. A Q4_K_M quant uses a four bit quantization method that balances size and quality. A Q6_K quant uses a six bit method that preserves more original model accuracy but requires more video memory.

With 8 GB of video memory, you can run several highly capable models entirely on your graphics card. For example, Gemma 2 9B fits at the Q4_K_M quantization level while using exactly 8 GB of video memory. Other models like Llama 3.1 8B fit comfortably at the Q5_K_M quantization level while using 7.4 GB. You can also run models like Mistral 7B or Qwen2.5 7B at the higher quality Q6_K quantization level, which use 7.4 GB and 6.9 GB of video memory respectively.

When a model is slightly too large for your video memory, you can use CPU offloading. This process splits the workload between your graphics card and your system memory. For instance, running FLUX.1 schnell requires 8.8 GB of video memory at the Q4_K_M quantization level. Because this exceeds your physical video memory, the system offloads the remaining portion to your system RAM, which requires 10.8 GB of system RAM to function.

CPU offloading allows you to run larger models like Vicuna 13B, which needs 9.5 GB of video memory at Q4_K_M and 11.5 GB of system RAM. However, this offloading process costs significant processing speed. The transfer speed of system RAM is much slower than GDDR6 video memory, so your generation speeds will drop. Keeping models fully within the 8 GB video memory limit is always recommended for real time tasks.

You must also consider the memory required for context. The memory usage figures listed are calculated using a standard 4k context window. If you increase the context window to process longer documents or extended conversations, the model will require additional video memory. For the best performance on your NVIDIA RTX 5050, you should choose a model that leaves a small memory buffer for this context data.