The DeepSeek-V3 model, with its 671 billion parameters, presents a significant challenge for the AMD RX 7900 XTX due to its massive VRAM requirement. Running DeepSeek-V3 in FP16 (half-precision floating point) mode requires approximately 1342GB of VRAM to load the entire model. The RX 7900 XTX, equipped with 24GB of GDDR6 memory, falls drastically short of this requirement, resulting in a VRAM deficit of 1318GB. This immense discrepancy prevents the model from even being loaded onto the GPU for inference. Memory bandwidth, while substantial on the RX 7900 XTX (0.96 TB/s), becomes irrelevant in this scenario since the model cannot fit within the available memory.
Without sufficient VRAM, the model cannot perform any meaningful computation. The absence of Tensor Cores on the RX 7900 XTX further complicates matters. Tensor Cores accelerate matrix multiplications, a core operation in deep learning, but are not available on this AMD GPU. While ROCm, AMD's software platform, could potentially be utilized, the primary bottleneck remains the insurmountable VRAM limitation. Consequently, performance metrics like tokens per second and achievable batch size are effectively zero, as the model simply cannot be run in its entirety on this GPU.
Given the extreme VRAM disparity, running DeepSeek-V3 directly on the RX 7900 XTX in FP16 is not feasible. To make this model runnable, aggressive quantization techniques are essential. Consider using 4-bit quantization (bitsandbytes or similar) which can reduce the VRAM footprint by a factor of approximately 4, bringing the requirement down to around 335.5GB. Even with this reduction, the model will still not fit on the 24GB RX 7900 XTX.
Therefore, explore offloading layers to system RAM. Frameworks like llama.cpp or ExllamaV2 allow you to split the model between the GPU and system memory, trading off speed for memory capacity. Be aware that this will drastically reduce inference speed, as data transfer between the GPU and system RAM becomes a bottleneck. Alternatively, consider using a smaller model or a cloud-based GPU with sufficient VRAM. Distributed inference across multiple GPUs is another option, but requires significant technical expertise and infrastructure.