The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM, falls significantly short of the 1342GB required to load the full DeepSeek-V3 (671B parameter) model in FP16 precision. This discrepancy arises because the model's parameters, when stored in FP16 (half-precision floating-point), consume a substantial amount of memory. The RTX 3090 Ti's memory bandwidth of 1.01 TB/s, while considerable, is also a limiting factor, as it dictates the speed at which data can be transferred between the GPU and memory. Even if the model could somehow fit into the available VRAM, the memory bandwidth could become a bottleneck, affecting inference speed.
Furthermore, the Ampere architecture of the RTX 3090 Ti, while equipped with 10752 CUDA cores and 336 Tensor cores, is designed for a different scale of model. DeepSeek-V3, being an extremely large language model, is optimized for distributed computing environments with multiple GPUs or specialized AI accelerators. Attempting to run it on a single RTX 3090 Ti will likely result in out-of-memory errors and prohibitively slow performance, rendering it practically unusable for real-time or interactive applications.
Given the VRAM limitations, directly running the full DeepSeek-V3 model on the RTX 3090 Ti is not feasible. Consider exploring model quantization techniques such as Q4 or even lower precisions to significantly reduce the VRAM footprint. Frameworks like `llama.cpp` are designed to run large models on consumer hardware using quantization. Alternatively, explore cloud-based inference services or distributed computing solutions where the model is split across multiple GPUs. If local execution is crucial, investigate smaller, distilled versions of the model that are specifically designed to run on resource-constrained hardware. Fine-tuning a smaller model on a relevant dataset might offer a more practical solution.
Another approach is to use CPU offloading, where parts of the model are processed on the CPU. However, this will significantly reduce performance. Consider upgrading to a system with more VRAM, or utilize cloud-based inference services that offer the necessary resources for running large models like DeepSeek-V3. Before attempting any local execution, carefully evaluate the trade-offs between model size, quantization level, and acceptable performance.