The AMD RX 7800 XT, with its 16GB of GDDR6 VRAM, falls significantly short of the 472GB VRAM required to load the DeepSeek-V2.5 model in FP16 precision. This massive discrepancy means the entire model cannot reside on the GPU's memory simultaneously. Consequently, direct inference is impossible without employing substantial offloading techniques. While the RX 7800 XT boasts a memory bandwidth of 0.62 TB/s, this bandwidth is insufficient to compensate for the constant data transfer between system RAM and the GPU that would be necessary if offloading were attempted. The absence of dedicated Tensor Cores on the RX 7800 XT further impacts performance, as it lacks specialized hardware to accelerate the tensor operations that are fundamental to deep learning models like DeepSeek-V2.5. The RDNA 3 architecture, while capable, is not optimized for the memory demands of models of this scale.
Even with aggressive quantization techniques, such as 4-bit or 2-bit quantization, the model's memory footprint will likely still exceed the available VRAM. While quantization reduces the memory required to store each parameter, the sheer size of DeepSeek-V2.5 (236 billion parameters) means that the reduced model will still be too large to fit. This memory limitation severely restricts the achievable batch size, potentially down to 1, and dramatically lowers the tokens processed per second. Without sufficient VRAM, the model will be forced to rely heavily on system RAM, leading to significant performance bottlenecks and rendering real-time or interactive applications impractical.
Given the severe VRAM limitation, running DeepSeek-V2.5 directly on the AMD RX 7800 XT is not feasible. Consider using cloud-based inference services that offer access to GPUs with sufficient VRAM. Alternatively, explore smaller language models that fit within the 16GB VRAM capacity of the RX 7800 XT. If you are determined to run DeepSeek-V2.5 locally, investigate extreme quantization methods combined with CPU offloading. Be aware that performance will be significantly degraded, making it suitable only for experimentation or very low-throughput applications.
If exploring local execution, use llama.cpp with a very low quantization level (e.g., Q2_K or even lower) and offload as many layers as possible to the CPU. Monitor VRAM usage closely and adjust the number of layers offloaded to the CPU to prevent system instability. Set a very small context length and batch size to minimize memory pressure. Realistically, expect very slow inference speeds and consider this approach only for educational purposes or proof-of-concept scenarios.