The primary limiting factor for running LLaVA 1.6 34B on an NVIDIA RTX 3090 Ti is the VRAM. LLaVA 1.6 34B, with 34 billion parameters, requires approximately 68GB of VRAM when using FP16 (half-precision floating point) for storing the model weights and intermediate activations during inference. The RTX 3090 Ti, while a powerful GPU, only offers 24GB of VRAM. This creates a significant shortfall of 44GB, meaning the model cannot be loaded entirely onto the GPU's memory, leading to a compatibility failure.
Furthermore, even if techniques like offloading layers to system RAM were employed, the performance would be severely hampered due to the comparatively slow transfer speeds between system RAM and the GPU. While the RTX 3090 Ti boasts a high memory bandwidth of 1.01 TB/s, this bandwidth is only applicable to data residing within its GDDR6X VRAM. Accessing system RAM would introduce significant latency and bottleneck the inference process, resulting in unacceptably slow token generation speeds. The 10752 CUDA cores and 336 Tensor Cores of the RTX 3090 Ti are rendered largely ineffective due to the VRAM constraint.
Due to the substantial VRAM deficit, running LLaVA 1.6 34B directly on the RTX 3090 Ti is impractical without significant modifications. Consider exploring quantization techniques such as Q4 or even lower precisions using libraries like `llama.cpp` or `AutoGPTQ`. This can drastically reduce the VRAM footprint of the model, potentially bringing it within the 24GB limit, albeit with some loss in accuracy. Another option involves using cloud-based GPU services or platforms that offer access to GPUs with sufficient VRAM, such as those with 80GB of VRAM or more.
If you choose to pursue local inference with quantization, be prepared for a reduction in model quality. Experiment with different quantization levels to find a balance between VRAM usage and acceptable performance. Monitor GPU utilization and token generation speed to assess the effectiveness of the chosen quantization method. Be aware that even with aggressive quantization, performance may still be slower compared to running the full-precision model on a GPU with adequate VRAM.