The DeepSeek-Coder-V2 model, with its 236 billion parameters, presents a significant challenge for the NVIDIA RTX 3090 Ti due to its substantial VRAM requirements. Specifically, running DeepSeek-Coder-V2 in FP16 (half-precision floating point) mode necessitates approximately 472GB of VRAM. The RTX 3090 Ti, equipped with 24GB of GDDR6X memory, falls far short of this requirement, resulting in a VRAM deficit of 448GB. This discrepancy makes it impossible to load the entire model onto the GPU for inference, leading to a compatibility failure. The high memory bandwidth of the RTX 3090 Ti (1.01 TB/s) would be beneficial if the model *could* fit, but it cannot overcome the fundamental limitation of insufficient memory capacity.
Beyond VRAM, the sheer size of the model impacts performance. Even if techniques like offloading layers to system RAM were employed (which would severely degrade performance), the computational demands of 236 billion parameters would strain the 10752 CUDA cores and 336 Tensor cores of the RTX 3090 Ti. Without significant model optimization, such as quantization, the RTX 3090 Ti would struggle to deliver acceptable inference speeds. Given the model's size and the GPU's memory constraints, estimating tokens per second or achievable batch sizes without extensive modifications is not feasible. The Ampere architecture of the RTX 3090 Ti is capable, but bottlenecked by memory.
Due to the extreme VRAM requirements of DeepSeek-Coder-V2, direct inference on an RTX 3090 Ti is not practical. To run this model, consider using cloud-based inference services that offer GPUs with sufficient VRAM, such as NVIDIA A100 or H100 instances. Alternatively, explore techniques like model quantization (e.g., using 4-bit or 8-bit quantization) and CPU offloading to reduce VRAM usage, but be aware that this will significantly impact inference speed. Distributed inference across multiple GPUs is another option, but it requires specialized software and hardware configurations.
For local experimentation, focus on smaller models that fit within the RTX 3090 Ti's VRAM capacity. If you are determined to run DeepSeek-Coder-V2 locally, thoroughly investigate quantization methods to compress the model as much as possible. Consider using inference frameworks optimized for low-resource environments, and be prepared for very slow inference speeds. Also, be mindful of the power consumption (TDP 450W) of the RTX 3090 Ti, especially when pushing it to its limits.