The NVIDIA RTX 4060 Ti 8GB falls significantly short of the VRAM requirements for the DeepSeek-Coder-V2 model. DeepSeek-Coder-V2, with its 236 billion parameters, necessitates approximately 472GB of VRAM when using FP16 (half-precision floating point) data type. The RTX 4060 Ti 8GB only provides 8GB of VRAM, resulting in a massive VRAM deficit of 464GB. This discrepancy makes it impossible to load the entire model onto the GPU for inference without employing advanced techniques like quantization or offloading layers to system RAM, which would drastically reduce performance.
Even if aggressive quantization techniques are applied, the model size remains a challenge. Memory bandwidth also becomes a bottleneck. The RTX 4060 Ti 8GB's memory bandwidth of 0.29 TB/s is insufficient for efficiently transferring the model's data during inference, further impacting the achievable tokens per second. The limited CUDA cores (4352) and Tensor Cores (136), while decent for smaller models, are inadequate to handle the computational demands of a model as large as DeepSeek-Coder-V2 without significant performance degradation. The Ada Lovelace architecture provides some efficiency improvements, but it cannot overcome the fundamental VRAM limitation.
Due to the severe VRAM shortage, running DeepSeek-Coder-V2 on the RTX 4060 Ti 8GB will likely result in out-of-memory errors, extremely slow inference speeds (if it runs at all), and an unusable experience. The estimated tokens per second and batch size are essentially zero in a direct, unoptimized scenario.
Directly running DeepSeek-Coder-V2 on an RTX 4060 Ti 8GB is not feasible. To potentially run the model, explore extreme quantization methods like 4-bit or even 2-bit quantization, understanding that this will significantly impact the model's accuracy. Consider using inference frameworks like `llama.cpp` or `text-generation-inference` that offer advanced quantization and offloading capabilities. Even with these optimizations, performance will likely be very slow, and the model's output quality may be noticeably degraded.
Alternatively, consider using cloud-based inference services or platforms that provide access to GPUs with sufficient VRAM, such as NVIDIA A100 or H100. Another option is to explore smaller, more manageable models that are better suited for your hardware. Fine-tuning a smaller model on a relevant coding dataset could provide a more practical solution for your needs.