The core issue lies in the VRAM requirements of the LLaVA 1.6 34B model. This model, in FP16 precision, demands approximately 68GB of VRAM to operate effectively. The NVIDIA RTX 3080, with its 10GB of VRAM, falls significantly short of this requirement. This discrepancy means the entire model and its intermediate computations cannot be loaded onto the GPU simultaneously, leading to out-of-memory errors or forcing the system to rely on significantly slower system RAM, drastically impacting performance. Memory bandwidth, while substantial on the RTX 3080 (0.76 TB/s), becomes a secondary concern when VRAM capacity is the primary bottleneck.
Furthermore, even if some clever memory management techniques were employed to partially load the model, the performance would likely be unacceptably slow. The constant swapping of model layers between system RAM and GPU VRAM would introduce massive latency. The 8704 CUDA cores and 272 Tensor cores on the RTX 3080 would be underutilized due to the data starvation caused by the VRAM limitation. Consequently, the expected tokens per second generated would be minimal, rendering real-time or interactive applications infeasible. The large parameter size of the model exacerbates the problem, requiring substantial computational resources that are further constrained by the lack of sufficient VRAM.
Unfortunately, running LLaVA 1.6 34B directly on an RTX 3080 10GB is not feasible due to the severe VRAM limitation. To work with this model, consider using cloud-based GPU services like NelsaHost that offer instances with sufficient VRAM (e.g., A100, H100). Alternatively, explore techniques like quantization to reduce the model's memory footprint. Quantization to INT8 or even lower precisions (e.g., 4-bit) can significantly decrease VRAM usage, but it may come at the cost of some accuracy. Another approach is to utilize CPU offloading, where some model layers are processed on the CPU, freeing up VRAM on the GPU, but this will drastically slow down inference speed. Distributed inference across multiple GPUs, while complex to set up, is another option if you have access to multiple machines.