The NVIDIA RTX 4060 Ti 16GB, while a capable mid-range GPU based on the Ada Lovelace architecture, falls significantly short of the VRAM requirements for running DeepSeek-Coder-V2 in its full FP16 precision. DeepSeek-Coder-V2, with its 236 billion parameters, necessitates approximately 472GB of VRAM for FP16 inference. The RTX 4060 Ti 16GB provides only 16GB of GDDR6 memory. This results in a massive VRAM deficit of 456GB, making direct loading and execution of the model impossible without substantial modifications. The RTX 4060 Ti's memory bandwidth of 290 GB/s, while decent, becomes a bottleneck even if the model could somehow be loaded, as frequent data transfers between system RAM and the GPU would severely throttle performance.
Even with aggressive quantization techniques, achieving acceptable performance with DeepSeek-Coder-V2 on the RTX 4060 Ti 16GB is highly unlikely. The model's size necessitates offloading significant portions to system RAM or even disk, leading to unacceptable latency. Furthermore, the 4352 CUDA cores and 136 Tensor cores of the RTX 4060 Ti, while beneficial for smaller models, are insufficient to compensate for the memory limitations when dealing with a model of this scale. Expect extremely low tokens per second or even out-of-memory errors during inference attempts.
Directly running DeepSeek-Coder-V2 on an RTX 4060 Ti 16GB is not feasible. Instead, consider using cloud-based inference services like those offered by NelsaHost or other providers, which offer access to GPUs with sufficient VRAM (e.g., A100, H100). Alternatively, explore model distillation or pruning techniques to create a smaller, more manageable model that can fit within the 16GB VRAM. For local execution, focus on smaller models that are specifically designed to run on consumer-grade hardware, such as models with parameter counts in the single-digit billions.
If you are determined to experiment with DeepSeek-Coder-V2 locally, investigate extreme quantization methods like 4-bit or even 2-bit quantization using libraries such as `bitsandbytes` or `llama.cpp`. Be prepared for significant performance degradation and potential accuracy loss. Offloading layers to CPU is another option, but it will further reduce inference speed. Realistically, the RTX 4060 Ti 16GB is not a suitable platform for this particular model.