The AMD RX 7800 XT, while a capable gaming GPU, falls significantly short of the VRAM requirements for running DeepSeek-Coder-V2. This model, with its massive 236 billion parameters, necessitates approximately 472GB of VRAM when using FP16 precision. The RX 7800 XT is equipped with only 16GB of GDDR6 VRAM, resulting in a deficit of 456GB. This vast discrepancy makes it impossible to load the entire model onto the GPU for inference. Furthermore, even if techniques like offloading to system RAM were employed, the relatively lower bandwidth of system RAM compared to GDDR6 would severely bottleneck performance, rendering inference speeds unacceptably slow.
Beyond VRAM limitations, the absence of dedicated Tensor Cores on the RX 7800 XT also impacts performance. Tensor Cores accelerate matrix multiplications, a core operation in deep learning. While the GPU can still perform these calculations using its CUDA cores, the lack of dedicated hardware leads to lower throughput and increased latency. Memory bandwidth, at 0.62 TB/s, also plays a crucial role; however, the primary bottleneck is the insufficient VRAM. The RDNA 3 architecture is not optimized for this kind of workload.
Due to the severe VRAM limitations, directly running DeepSeek-Coder-V2 on the AMD RX 7800 XT is not feasible. Consider exploring smaller, more manageable models that fit within the 16GB VRAM constraint. Alternatively, investigate cloud-based solutions or services that offer access to GPUs with sufficient VRAM, such as NVIDIA A100 or H100 instances. If you are determined to run DeepSeek-Coder-V2 locally, explore distributed inference techniques across multiple GPUs, although this requires significant technical expertise and specialized software.