The DeepSeek-V2.5 model, with its 236 billion parameters, presents a significant challenge for the NVIDIA RTX 4090 due to its substantial VRAM requirement. Running DeepSeek-V2.5 in FP16 (half-precision floating point) necessitates approximately 472GB of VRAM. The RTX 4090, equipped with 24GB of GDDR6X VRAM, falls drastically short of this requirement, resulting in a VRAM deficit of 448GB. This discrepancy prevents the model from being loaded and executed directly on the GPU without employing advanced techniques to reduce memory footprint.
Memory bandwidth, while substantial on the RTX 4090 at 1.01 TB/s, becomes a secondary concern when the model cannot even fit into the available VRAM. Even if the model could be squeezed into the 24GB, the limited memory capacity would severely restrict the effective batch size and context length, leading to extremely poor performance. The 16384 CUDA cores and 512 Tensor cores of the RTX 4090 would remain largely underutilized due to the VRAM bottleneck, making inference impractical without significant modifications.
Given the VRAM constraints, direct inference of DeepSeek-V2.5 on a single RTX 4090 is not feasible. The model's size overwhelms the GPU's capacity, rendering it unable to perform computations efficiently. Expected performance metrics such as tokens per second and achievable batch sizes are effectively zero in this scenario without employing techniques to reduce the model's memory footprint.
To run DeepSeek-V2.5, or similarly large models, on hardware with limited VRAM like the RTX 4090, consider these strategies: Model Quantization, Offloading, and Distributed Inference. Quantization reduces the precision of the model's weights (e.g., to 4-bit or 8-bit), significantly decreasing the VRAM footprint. Frameworks like `llama.cpp` and `text-generation-inference` support quantization. Offloading involves moving parts of the model to system RAM or even disk, trading off speed for memory. Distributed inference spreads the model across multiple GPUs, aggregating their VRAM. For example, using a framework like `vLLM` with multiple GPUs could allow DeepSeek to run with acceptable performance.
For the RTX 4090 specifically, aggressive quantization (e.g., Q4_K_M) combined with CPU offloading might allow a small context length to be used, but performance will be severely impacted. A more viable approach is to explore smaller models that fit within the RTX 4090's VRAM or invest in a multi-GPU setup. Before investing in a multi-GPU setup, consider renting compute from cloud providers to test your application.