The DeepSeek-Coder-V2 model, with its 236 billion parameters, presents a significant challenge for the NVIDIA RTX 4060 due to its massive VRAM requirement. In FP16 (half-precision floating point), the model necessitates approximately 472GB of VRAM to load and operate. The RTX 4060, equipped with only 8GB of VRAM, falls drastically short of this requirement, resulting in a VRAM headroom deficit of 464GB. This discrepancy means the model cannot be loaded onto the GPU in its native FP16 format. Memory bandwidth also plays a crucial role; even if VRAM limitations were somehow circumvented, the RTX 4060's 0.27 TB/s memory bandwidth would likely become a bottleneck, severely limiting the model's inference speed.
Due to the extreme VRAM shortage, running DeepSeek-Coder-V2 directly on the RTX 4060 is not feasible without significant compromises. Attempting to load the model would lead to out-of-memory errors. Even with techniques like offloading layers to system RAM, the performance would be unacceptably slow due to the constant data transfer between the GPU and system memory via the relatively slow PCIe bus. Therefore, the expected tokens per second and batch size on this configuration would be minimal, rendering it impractical for real-time or even near real-time applications.
Given the severe VRAM limitations, directly running DeepSeek-Coder-V2 on the RTX 4060 is not recommended. Several alternative approaches can be considered, but each involves trade-offs. The most viable option is to utilize aggressive quantization techniques, such as Q4 or even lower precisions, to significantly reduce the model's memory footprint. Frameworks like `llama.cpp` are well-suited for this purpose, enabling CPU-based inference with quantized models. Alternatively, consider cloud-based inference services or renting a GPU with sufficient VRAM (e.g., an NVIDIA A100 or H100) if performance is critical.
If you choose to proceed with the RTX 4060, focus on minimizing the context length to the bare minimum needed for your task. Experiment with extremely small batch sizes (possibly even 1) and monitor system RAM usage closely to avoid crashes. Be prepared for very slow inference speeds, potentially several seconds or even minutes per token. Finally, ensure your system has ample system RAM (at least 64GB) and a fast NVMe SSD to mitigate the performance impact of offloading.