The DeepSeek-V3 model, with its 671 billion parameters, requires an enormous amount of VRAM to operate effectively, especially when using FP16 (half-precision floating point) for reduced memory footprint. The model's VRAM requirement is approximately 1342GB. The NVIDIA RTX 4060 Ti 8GB, in contrast, provides only 8GB of VRAM. This creates a massive shortfall of 1334GB, making direct loading and inference of the full DeepSeek-V3 model on this GPU impossible without significant modifications.
Even if the model could be squeezed into the available VRAM, the memory bandwidth of the RTX 4060 Ti (0.29 TB/s) would become a bottleneck. Large language models like DeepSeek-V3 benefit significantly from high memory bandwidth to efficiently transfer weights and intermediate activations during computation. Insufficient bandwidth would lead to drastically reduced throughput and increased latency, making real-time or interactive applications impractical. The limited number of CUDA and Tensor cores on the RTX 4060 Ti, while capable, are further constrained by the memory limitations.
Given the substantial VRAM discrepancy, running DeepSeek-V3 directly on the RTX 4060 Ti 8GB is not feasible. Consider using cloud-based inference services or platforms that offer access to GPUs with sufficient VRAM. Alternatively, explore techniques like quantization (e.g., using 4-bit or even lower precision) and model sharding across multiple GPUs, although these methods introduce complexity and potential performance trade-offs. For local use, smaller models like those in the 7B to 30B parameter range may be more suitable for your GPU.
If you're determined to experiment locally, look into offloading layers to system RAM, but be aware this will significantly reduce inference speed. Focus on highly optimized inference frameworks like llama.cpp with appropriate quantization settings to maximize performance within the hardware limitations. Always monitor VRAM usage closely during experimentation to avoid out-of-memory errors.