The AMD RX 7900 XTX, with its 24GB of GDDR6 VRAM, falls significantly short of the VRAM requirements for running Llama 3 70B in FP16 precision, which demands approximately 140GB. This massive discrepancy means the entire model cannot be loaded onto the GPU simultaneously. Memory bandwidth, while substantial at 0.96 TB/s, becomes a secondary concern when the primary issue is insufficient VRAM. The absence of Tensor Cores on the RX 7900 XTX further limits its ability to accelerate the model. Consequently, direct inference using FP16 precision is not feasible.
While the RX 7900 XTX offers a strong RDNA 3 architecture and a decent number of compute units (6144 CUDA cores equivalent), the bottleneck is clearly VRAM. Attempting to run the model without addressing the VRAM issue will result in out-of-memory errors. Even if techniques like CPU offloading are employed, performance will be severely degraded due to the slow transfer speeds between system RAM and the GPU. The absence of hardware-accelerated tensor operations also contributes to the expected poor performance.
To run Llama 3 70B on the RX 7900 XTX, you must significantly reduce the model's memory footprint. This can be achieved through quantization, specifically using techniques like 4-bit or 8-bit quantization. Frameworks like llama.cpp are well-suited for this purpose. Even with quantization, expect performance to be considerably slower compared to GPUs with sufficient VRAM. Explore options like splitting the model across multiple GPUs if available, or offloading some layers to system RAM, but be aware that these approaches will further impact performance.
Before attempting to run the model, thoroughly research and implement the chosen quantization method. Experiment with different quantization levels to find a balance between memory usage and output quality. Monitor VRAM usage closely to ensure you don't exceed the available capacity. Due to the limited VRAM, even with quantization, a smaller context length and batch size will likely be necessary to avoid out-of-memory errors.