The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM, is a powerful GPU, but it falls short of the 26GB VRAM required to run the LLaVA 1.6 13B model in FP16 precision. This 2GB deficit means the model, in its default configuration, cannot be loaded entirely onto the GPU, leading to out-of-memory errors. The RTX 4090 boasts a memory bandwidth of 1.01 TB/s and 16384 CUDA cores, which would otherwise provide excellent performance for AI inference tasks. However, the primary bottleneck here is the insufficient VRAM, preventing the model from fully utilizing the GPU's computational capabilities.
While the RTX 4090's Ada Lovelace architecture and 512 Tensor Cores are designed for accelerating AI workloads, the VRAM limitation will force the system to rely on slower system memory (RAM) or even disk storage, drastically reducing inference speed. This can result in significantly lower tokens/second and severely limit the achievable batch size, making real-time or interactive applications impractical. The incompatibility stems directly from the model's size exceeding the GPU's memory capacity, regardless of the GPU's other performance characteristics.
To run LLaVA 1.6 13B on an RTX 4090, you'll need to reduce the model's memory footprint. The most effective method is to use quantization, such as converting the model to 8-bit integers (INT8) or even 4-bit integers (INT4). This can significantly reduce the VRAM requirement, potentially bringing it within the 24GB limit. Be aware that quantization may slightly impact the model's accuracy, but the trade-off is often acceptable for the ability to run the model at all.
Another approach is to offload some layers of the model to system RAM. Frameworks like `llama.cpp` allow for this, but it will significantly slow down inference. If performance is critical and quantization isn't sufficient, consider using a cloud-based GPU with more VRAM or distributing the model across multiple GPUs using model parallelism. You could also explore smaller models or fine-tune a smaller model for your specific task.