The AMD RX 7900 XTX, with its 24GB of GDDR6 VRAM, is well-suited for running the Llama 3 8B model. Llama 3 8B in FP16 precision requires approximately 16GB of VRAM, leaving a comfortable 8GB headroom for other operations and larger batch sizes. While the RX 7900 XTX lacks dedicated Tensor Cores found in NVIDIA GPUs, its substantial memory bandwidth of 0.96 TB/s ensures efficient data transfer between the GPU and memory, which is crucial for LLM inference. The RDNA 3 architecture provides a solid foundation for compute tasks, although performance may differ compared to NVIDIA GPUs due to architectural differences in handling matrix multiplications and other operations common in deep learning.
To maximize performance, consider using inference frameworks optimized for AMD GPUs, such as llama.cpp with the ROCm backend or ONNX Runtime. Experiment with quantization techniques, such as Q4 or Q5, to potentially reduce VRAM usage and increase inference speed without significant loss in accuracy. Start with a batch size of 5 and adjust based on your specific needs and available VRAM. Monitoring GPU utilization and temperature is recommended to ensure optimal performance and prevent overheating.