The NVIDIA RTX 6000 Ada, with its 48GB of GDDR6 VRAM, is a powerful GPU suitable for many AI tasks. However, the LLaVA 1.6 34B model presents a challenge due to its substantial memory footprint. Running LLaVA 1.6 34B in FP16 (half-precision floating point) requires approximately 68GB of VRAM. This stems from the model's 34 billion parameters, each requiring space to store weights, activations, and intermediate calculations during inference. The RTX 6000 Ada falls short by 20GB, making direct loading of the model in FP16 impossible.
While the RTX 6000 Ada boasts a memory bandwidth of 0.96 TB/s and a considerable number of CUDA and Tensor cores, these strengths are irrelevant if the model cannot fit into the available VRAM. Without sufficient VRAM, the system would resort to swapping data between the GPU and system RAM, drastically reducing performance. This swapping negates the benefits of the GPU's high memory bandwidth and parallel processing capabilities, rendering the model practically unusable for real-time or interactive applications.
To run LLaVA 1.6 34B on the RTX 6000 Ada, you'll need to significantly reduce the model's memory footprint. The most effective approach is quantization. Consider using 4-bit or 8-bit quantization techniques via libraries like `llama.cpp` or frameworks like vLLM. Quantization reduces the precision of the model's weights, thereby decreasing the VRAM requirement. However, this will likely lead to a minor reduction in accuracy, so testing is crucial to find a balance.
Alternatively, consider using a framework that supports offloading layers to system RAM or disk. While this will severely impact performance, it might allow you to experiment with the model. Finally, distributed inference across multiple GPUs is an option if you have access to more hardware, but this requires significant setup and expertise. If neither of these solutions is feasible, consider using a smaller model variant or a cloud-based inference service.