The primary limiting factor in running large language models (LLMs) like DeepSeek-V3 is the GPU's VRAM. DeepSeek-V3, with its 671 billion parameters, requires an immense amount of memory to load and operate efficiently. Specifically, in FP16 (half-precision floating point) format, the model needs approximately 1342GB of VRAM. The NVIDIA RTX 4080 SUPER, while a powerful card, only has 16GB of VRAM. This creates a massive shortfall of 1326GB, preventing the model from being loaded in its entirety onto the GPU. Consequently, standard inference is impossible without significant modifications. Memory bandwidth, at 0.74 TB/s, would also become a bottleneck even if the model could be loaded, as swapping data in and out of VRAM from system RAM would drastically reduce performance.
Due to the VRAM limitation, the RTX 4080 SUPER cannot directly run DeepSeek-V3. Even with techniques like quantization (reducing the precision of the model's weights), the model's size remains a significant hurdle. Quantization to INT8 (8-bit integer) or even lower precision formats like INT4 can reduce the VRAM footprint, but the base requirement is still substantial. The limited VRAM also impacts the feasible batch size and context length. A larger batch size requires more VRAM to store intermediate computations, and a longer context length increases the memory needed to track the sequence of tokens. Without sufficient VRAM, the model will either crash due to out-of-memory errors or perform extremely slowly due to constant swapping between the GPU and system RAM.
Given the VRAM constraints, running DeepSeek-V3 on an RTX 4080 SUPER is not feasible without significant compromises. Consider using a smaller model that fits within the 16GB VRAM limit, or explore cloud-based solutions that offer GPUs with much larger memory capacities. If you are determined to run DeepSeek-V3 locally, extreme quantization techniques combined with CPU offloading may be necessary, but the performance will likely be unacceptably slow for most applications. Distributed inference across multiple GPUs, while technically possible, would require significant engineering effort and a specialized software setup.
If you're experimenting, focus on aggressive quantization (e.g., using GPTQ or AWQ to quantize to 4-bit or even lower) and offload as many layers as possible to the CPU. Use an inference framework like `llama.cpp` which is optimized for CPU offloading. Be prepared for extremely slow inference speeds, possibly several minutes per token. A more practical approach might be to explore smaller, more efficient models that are designed to run on consumer-grade hardware.